A drone self-organizing network cluster management system based on smart streetlights and satellite communication

Abstract

This invention relates to the technical field of UAV self-organizing network cluster management and control scheme design based on smart streetlights and satellite communication, specifically to a UAV self-organizing network cluster management and control system based on smart streetlights and satellite communication. It includes a smart streetlight subsystem, a satellite communication subsystem, UAV cluster nodes, and a dynamic collaborative control platform. The innovative scheme includes: (1) a multi-mode communication gateway to achieve seamless conversion between satellite protocols and terrestrial IoT protocols; (2) streetlight sensors generating dynamic risk maps to guide cluster obstacle avoidance; (3) a fast clustering algorithm based on geometric topology to support emergency networking during signal interruptions; (4) task priority-driven time slot allocation to improve communication efficiency; and (5) a multi-level redundancy system based on satellite, streetlight, and relay UAVs to ensure continuous controllability. This scheme significantly enhances the communication reliability, environmental adaptability, and collaborative capabilities of the cluster in complex urban areas.

Description

Technical Field

[0001] This invention relates to the technical field of drone self-organizing network cluster management and control scheme design based on smart streetlights and satellite communication, specifically to a drone self-organizing network cluster management and control system based on smart streetlights and satellite communication. Background Technology

[0002] Existing technologies for managing and controlling drone swarms in smart cities suffer from multiple shortcomings. Satellite communication, the mainstream remote control method, is frequently interrupted at low altitudes due to the obstruction effect of tall buildings, making it difficult to ensure continuous communication for drones in complex urban areas. Terrestrial communication networks rely on cellular base stations, resulting in coverage blind spots and susceptibility to sudden interference, failing to meet the high-speed maneuverability requirements of swarms. While smart streetlight systems offer the advantage of wide distribution, their sensing capabilities are limited to environmental monitoring, failing to effectively coordinate with drone swarm control and creating "data silos." More significantly, there is poor compatibility with heterogeneous network protocols: satellite communication's spatial data protocols and terrestrial IoT protocols are not independent, requiring multiple encapsulation and decapsulation processes for cross-network data transmission, introducing significant latency and packet loss risks. The lack of multi-level redundancy mechanisms can easily lead to swarm control misalignment during extreme weather or equipment failures. Although some research has attempted to enhance network resilience through relay drones, it has not fully utilized urban infrastructure resources, and dynamic topology reconstruction is inefficient, making it difficult to support precise collaborative operations of large-scale swarms in densely populated areas.

[0003] Therefore, existing technologies still need further development. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a drone self-organizing network cluster management system based on smart streetlights and satellite communication, so as to solve the problems existing in the prior art.

[0005] To achieve the above technical objectives, this invention provides a drone self-organizing network cluster management system based on smart streetlights and satellite communication, comprising:

[0006] The intelligent street light subsystem includes a multi-mode communication gateway, an environmental sensor array, and a self-organizing network module;

[0007] The satellite communication subsystem is used to provide wide-area communication via a low-Earth orbit satellite constellation;

[0008] The drone swarm node is equipped with a dual-frequency communication module for simultaneous access to streetlights and satellite networks;

[0009] The dynamic collaborative control platform is used to integrate street light sensor data and satellite positioning data in real time to generate cluster task instructions.

[0010] Specifically, the multi-mode communication gateway has an embedded protocol converter for bidirectional conversion between the satellite-specific space data packet protocol and the IEEE 802.15.4 protocol of the street light network.

[0011] Specifically, the environmental sensor array includes features such as lidar, temperature and humidity sensors, and crowd density cameras. The data collected by the environmental sensor array is used to generate a gridded environmental risk map via edge computing nodes.

[0012] Specifically, the dual-frequency communication module integrates a link switching logic unit, which automatically connects to the triangular communication cluster formed by the three nearest streetlights when the satellite signal strength is lower than the threshold X.

[0013] Specifically, the dynamic calculation method for the threshold X is as follows:

[0014]

[0015] in, For minimum receiving power, Noise figure Boltzmann's constant, Absolute temperature For bandwidth.

[0016] Specifically, the dynamic collaborative control platform includes:

[0017] The task assignment engine divides drones into task groups based on a risk map.

[0018] Specifically, the dynamic collaborative control platform includes:

[0019] The path dynamic optimizer recalculates the cluster path at preset intervals to avoid high-risk areas.

[0020] Specifically, the system also includes an emergency relay drone, which uses a delay-tolerant network module carried by the emergency relay drone to store and forward critical instructions when both the satellite and street light networks fail simultaneously.

[0021] Specifically, the delay-tolerant network module employs the infection routing algorithm, and the message replica diffusion formula is:

[0022]

[0023] in, Maximum number of hops, TTL is time to live. This is the preset maximum number of replicas.

[0024] Specifically, the communication topology of the self-organizing network module adopts a time-division frequency-division multiple access mechanism to allocate dynamic time slots for the UAV swarm:

[0025]

[0026] in, For frame period, For the number of drones, Weighting coefficients for task priority.

[0027] Beneficial effects:

[0028] This system achieves a breakthrough improvement in cluster management technology by integrating smart streetlights with satellite communication capabilities:

[0029] 1. Innovation in communication reliability:

[0030] Multi-mode gateways break down the barriers between satellite and terrestrial network protocols, enabling seamless switching between dual-domain communication. Smart streetlights and drones form a dynamic self-organizing network, completely eliminating communication blind spots caused by building obstructions and ensuring full-time connectivity of the cluster.

[0031] 2. Upgraded intelligent environmental sensing:

[0032] Streetlight sensor arrays construct real-time dynamic risk maps, providing drones with centimeter-level environmental situational awareness. Deep collaboration between task allocation and path planning modules enables the cluster to possess proactive obstacle avoidance and situational response capabilities.

[0033] 3. System resilience significantly enhanced:

[0034] The four-level redundancy architecture integrates the satellite main link, street light backup link, relay drone emergency link, and local decision-making module, establishing a hierarchical fault takeover mechanism. Dual-mode hot-switching technology ensures that cluster control is not interrupted by any single point of failure.

[0035] 4. Optimize resource utilization:

[0036] The dynamic time slot allocation mechanism based on task priority enables intelligent on-demand allocation of communication bandwidth. This ensures the exclusive use of resources for critical tasks while improving the overall utilization of spectrum resources, effectively supporting ultra-large-scale cluster collaboration.

[0037] 5. Reusing facilities to reduce costs and increase efficiency:

[0038] Deeply reusing the city's smart street light network avoids the construction costs of dedicated communication base stations. Street light sensing data directly drives cluster decision-making, significantly enhancing the overall value of urban infrastructure. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the system composition of the UAV self-organizing network cluster management system based on smart streetlights and satellite communication provided in a specific embodiment of the present invention;

[0040] Figure 2This is a schematic diagram of the construction process of the dynamic risk map provided in a specific embodiment of the present invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0042] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0043] Please see Figures 1-2 This invention provides a drone self-organizing network cluster management system based on smart streetlights and satellite communication, comprising:

[0044] The intelligent street light subsystem 100 includes a multi-mode communication gateway, an environmental sensor array, and a self-organizing network module.

[0045] It should be noted that each smart street light is equipped with a smart street light subsystem 100. The deployment standards of the smart street light subsystem 100 can be specifically set according to the actual needs of the user. The present invention preferably designs the deployment standards of the smart street light subsystem 100 as follows:

[0046] 1. Installation spacing: 25±5m, meeting the 5.8GHz signal strength requirement of -70dBm;

[0047] 2. Height Configuration: The preferred height of the main light pole is 8m, balancing lighting and signal coverage;

[0048] 3. Communication module: 6m above the ground to avoid ground obstruction;

[0049] 4. Power supply redundancy: Dual mains power supply + solar cells, with the solar cells preferably having a power output of 120W to ensure 72 hours of power outage recovery.

[0050] Satellite communication subsystem 200 is used to provide wide-area communication via a low-Earth orbit satellite constellation;

[0051] It should be noted that the preferred satellite communication configuration of this invention is shown in Table 1:

[0052] Table 1 Satellite Communication Configuration

[0053]

[0054] It should be further noted that the satellite communication subsystem 200 described in this invention preferably adopts a low-Earth orbit satellite constellation (orbital altitude 500-800km), with an operating frequency band of 26.5-40GHz (Ka band), and the equivalent isotropic radiated power (EIRP) of the satellite terminal is 33dBW. The above settings were obtained by the technical personnel of this invention through a large number of tests, which can better guarantee the communication effect.

[0055] The drone swarm node 300 is equipped with a dual-frequency communication module for simultaneous access to streetlights and satellite networks;

[0056] It should be noted that the maximum flight speed of the UAV cluster node 300 is 20m / s (72km / h), and it is equipped with a dual-frequency communication module (2.4GHz / 5.8GHz) and an RTK-GPS positioning system (accuracy <0.5m). The above settings were obtained by the technicians of this invention through a large number of tests, which can ensure good communication performance.

[0057] The Dynamic Collaborative Control Platform 400 is used to fuse street light sensor data and satellite positioning data in real time to generate cluster task instructions.

[0058] It should be noted that the dynamic collaborative control platform 400 is deployed on an edge computing node with a response latency of ≤100ms, and processes sensor data and positioning information in real time.

[0059] Specifically, the multi-mode communication gateway has an embedded protocol converter for bidirectional conversion between the satellite-specific space data packet protocol and the IEEE 802.15.4 protocol of the street light network;

[0060] It should be further explained that the protocol conversion process is performed in three steps:

[0061] 1. Packet header stripping: Remove the 6-byte packet header (AX.25 frame structure) of the satellite CCSDS protocol to extract the payload data;

[0062] 2. Payload encapsulation: Add a MAC header according to the IEEE 802.15.4 protocol (including microsecond-level timestamp, source address, and destination address);

[0063] 3. Checksum appending: Calculate a 32-bit CRC checksum (generating polynomial 0x04C11DB7) and append it to the end of the data packet;

[0064] Specifically, the conversion delay is less than or equal to 50 milliseconds, which meets the 100ms control cycle requirement of the UAV, and the 100ms control cycle requirement of the UAV includes completing the conversion in half a cycle.

[0065] Specifically, the environmental sensor array includes features such as lidar, temperature and humidity sensors, and a crowd density camera. The data collected by the environmental sensor array is used to generate a gridded environmental risk map via edge computing nodes.

[0066] It should be further explained that, regarding environmental risk map modeling, the solution designed in this invention includes:

[0067] 1. Design mesh modeling specifications:

[0068] Cell size: 1.8m × 1.8m, chosen because it is smaller than the minimum rotor pitch of the drone;

[0069] Refresh rate: 5Hz, chosen to match the drone's maximum angular velocity of 30° / s;

[0070] 2. Design a risk value calculation model:

[0071]

[0072] in:

[0073] The dynamic risk value at target node j;

[0074] Obstacle density (the ratio of the area occupied by obstacles within a cell);

[0075] People density (value min(1, actual number of people / 5), saturation occurs at 5 people / m²);

[0076] Temperature gradient ( (25°C is the ideal temperature);

[0077] Weighting coefficients: , , (Thousands of simulations have verified that the collision rate is minimized).

[0078] It should be further noted that the preferred parameters of the lidar, temperature and humidity sensor, and optical camera of this invention are shown in Table 2:

[0079] Table 2 Sensor Parameters:

[0080]

[0081] Specifically, the dual-frequency communication module integrates a link switching logic unit, which automatically connects to the triangular communication cluster formed by the three nearest streetlights when the satellite signal strength is lower than the threshold X.

[0082] It should be further noted that regarding the link switching logic unit, the solution designed in the present invention includes:

[0083] 1. Design a three - level switching strategy:

[0084] ① Normal mode (RSSI > 10dB): Maintain the satellite link;

[0085] ② Early warning mode (8dB < RSSI ≤ 10dB): Pre - connect to the three nearest street lamps;

[0086] ③ Switching mode (RSSI ≤ 8dB): Access the street - lamp triangular communication cluster.

[0087] 2. Construction of the triangular communication cluster:

[0088] ① Screen the 10 candidate street lamps with the strongest signals;

[0089] ② Calculate the area of the triangle formed by every three street lamps (area threshold > 100m²);

[0090] ③ Select the triangle with the largest sum of signal intensities;

[0091] Threshold basis: When measured, the bit error rate increases steeply when RSSI ≤ 8dB (exceeding the tolerance of the control instruction by 10 -3 ).

[0092] Specifically, the dynamic calculation method of the threshold X is:

[0093]

[0094] Where, is the minimum received power, is the noise factor, is the Boltzmann constant, is the absolute temperature, is the bandwidth;

[0095] In the preferred embodiment of the present invention, the preferred values of the calculation parameters of the threshold X and the preferred basis are shown in Table 3:

[0096] Table 3 Preferred values of the calculation parameters of the threshold X and the preferred basis

[0097]

[0098] Substitute into the calculation to obtain which is approximately equal to 7.96dB (rounded to 8dB).

[0099] Specifically, the dynamic collaborative control platform 400 includes:

[0100] A task assignment engine that divides the UAVs into task groups based on the risk map.

[0101] Specifically, the dynamic collaborative control platform 400 includes:

[0102] The path dynamic optimizer recalculates the cluster path at preset intervals to avoid high-risk areas.

[0103] It should be further explained that, regarding the path dynamic optimizer, the scheme designed in this invention includes:

[0104] The parameters of the improved ant colony algorithm are shown in Table 4:

[0105] Table 4 Parameters of the Improved Ant Colony Algorithm

[0106]

[0107] State transition probability formula:

[0108]

[0109] in:

[0110] Let $k$ be the probability that drone $k$ moves from node $i$ to node $j$.

[0111] Let be the pheromone concentration along path (i, j);

[0112] The pheromone influence weight index is preferably 1.2 in this invention, which is used to amplify the effect of pheromones (strengthening positive feedback when α>1).

[0113] Let i be the heuristic function value from i to j;

[0114] The influence weighting index of the heuristic function is preferably 2.5 in this invention, used to enhance the effect of real-time environmental factors. Greater than The choice is made with a focus on security;

[0115] This represents the set of neighboring nodes that the drone can currently select.

[0116] Wherein the heuristic function is:

[0117]

[0118] The coefficient a = 0.7 represents the preferred weighting coefficient for distance cost, and b = 0.3 represents the preferred weighting coefficient for risk cost. It should be noted that this invention has been verified through 5000 Monte Carlo simulations, and when a = 0.7 and b = 0.3:

[0119] The collision rate was reduced to 0.3% (compared to 1.2% when a=b=0.5);

[0120] The path length increases by only 8.7% (the best balance between safety and efficiency).

[0121] Let be the Euclidean distance from node i to node j;

[0122] The dynamic risk value at target node j is derived from real-time data from street light sensors (the higher the value, the more dangerous it is).

[0123] Update cycle: 2 seconds (synchronizing the drone navigation system refresh rate, a single ant colony optimization takes about 1.2 seconds (Intel i7-11800H), with a 0.8-second communication delay margin).

[0124] It should be noted here that the collaborative working mechanism of the above formulas includes:

[0125] 1. Probability calculation:

[0126] molecular Used to enhance risk aversion ( );

[0127] Denominator normalization is used to ensure a reasonable allocation of probabilities in multi-path competition;

[0128] 2. Dynamic optimization:

[0129] when A sudden increase (such as a sudden gathering of people). Sharply decrease The risk is significantly reduced, at which point the drone will actively detour.

[0130] 3. Long-term learning:

[0131] Release pheromones along the successful path. This experience will help guide subsequent drones to choose safer routes.

[0132] The above solution will be illustrated below through specific engineering application examples:

[0133] Suppose a drone needs to move from node A to node B:

[0134] Path 1: If Risk = 0.1, then ;

[0135] Path 2: If Risk = 0.02, then ;

[0136] If the pheromone concentration of path 1 It is 1.2 times that of path 2.

[0137] ;

[0138] ;

[0139] Result: The probability of choosing path 1 is 70% (although it is slightly shorter, it is riskier), reflecting the system's emphasis on security.

[0140] Specifically, the system also includes an emergency relay drone, which uses a delay-tolerant network module carried by the emergency relay drone to store and forward critical instructions when both the satellite and street light networks fail simultaneously.

[0141] It should be further explained that, regarding the latency-tolerant network architecture, the solution designed in this invention includes:

[0142] Dedicated relay drone has a flight time of ≥90 minutes and a storage capacity of 64GB;

[0143] Message Replication Control Model:

[0144]

[0145] in:

[0146] (Tests show that latency >10 hops is uncontrollable);

[0147] TTL = 300s (covering the typical task cycle);

[0148] (Neighbors discovered the gap);

[0149] (Replication limit; exceeding this limit increases the probability of network congestion by 46%).

[0150] Implementation process:

[0151] 1. Relay drones patrol the network edge;

[0152] 2. Activate the infection route when a link interruption is detected;

[0153] 3. Broadcast storage instructions according to the replica count model.

[0154] Specifically, the delay-tolerant network module employs the infection routing algorithm, and the message replica diffusion formula is:

[0155]

[0156] in, Maximum number of hops, TTL is time to live. This is the preset maximum number of replicas.

[0157] Specifically, the communication topology of the self-organizing network module adopts a time-division frequency-division multiple access mechanism to allocate dynamic time slots for the UAV swarm:

[0158]

[0159] in, For frame period, For the number of drones, Weighting coefficients for task priority.

[0160] It should be further explained that, regarding the time slot allocation mechanism, the scheme designed in this invention includes:

[0161] 1. Design the dynamic time slot calculation formula:

[0162]

[0163] Variable definition:

[0164] (Frame period, satisfying the persistence of vision in the human eye);

[0165] Number of drones in the current communication domain;

[0166] Task priority coefficient (determined by referring to a table, as shown in Table 5):

[0167] Table 5 Task Priority Coefficients

[0168]

[0169] Allocation rules:

[0170] 1. Each drone acquires a basic time slot: ;

[0171] in, This indicates the number of drones in the current communication domain.

[0172] 2. Increase time slot resources by multiplying according to task priority coefficients;

[0173] Specifically, time slot resources are multiplied using the following formula:

[0174]

[0175] in:

[0176] For the original time slot;

[0177] The time slot after doubling;

[0178] Task priority coefficient (determined by looking up a table);

[0179] Bandwidth utilization factor ( , (For the number of high-priority drones);

[0180] Specifically, an example of a priority decision matrix is ​​shown in Table 6:

[0181] Table 6 Priority Decision Matrix

[0182]

[0183] Conflict prevention mechanism:

[0184] When there are too many high-priority tasks (preferred in this invention) hour):

[0185] Activate excess factor ;

[0186] Actual allocation: .

[0187] 3. Idle time slots are dynamically allocated to high-priority tasks.

[0188] Understandably, this system achieves a breakthrough improvement in cluster management technology by integrating smart streetlights with satellite communication capabilities.

[0189] 1. Innovation in communication reliability:

[0190] Multi-mode gateways break down the barriers between satellite and terrestrial network protocols, enabling seamless switching between dual-domain communication. Smart streetlights and drones form a dynamic self-organizing network, completely eliminating communication blind spots caused by building obstructions and ensuring full-time connectivity of the cluster.

[0191] 2. Upgraded intelligent environmental sensing:

[0192] Streetlight sensor arrays construct real-time dynamic risk maps, providing drones with centimeter-level environmental situational awareness. Deep collaboration between task allocation and path planning modules enables the cluster to possess proactive obstacle avoidance and situational response capabilities.

[0193] 3. System resilience significantly enhanced:

[0194] The four-level redundancy architecture integrates the satellite main link, street light backup link, relay drone emergency link, and local decision-making module, establishing a hierarchical fault takeover mechanism. Dual-mode hot-switching technology ensures that cluster control is not interrupted by any single point of failure.

[0195] 4. Optimize resource utilization:

[0196] The dynamic time slot allocation mechanism based on task priority enables intelligent on-demand allocation of communication bandwidth. This ensures the exclusive use of resources for critical tasks while improving the overall utilization of spectrum resources, effectively supporting ultra-large-scale cluster collaboration.

[0197] 5. Reusing facilities to reduce costs and increase efficiency:

[0198] Deeply reusing the city's smart street light network avoids the construction costs of dedicated communication base stations. Street light sensing data directly drives cluster decision-making, significantly enhancing the overall value of urban infrastructure.

[0199] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0200] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A drone self-organizing network cluster management and control system based on smart streetlights and satellite communication, characterized in that, The system includes: The intelligent street light subsystem includes a multi-mode communication gateway, an environmental sensor array, and a self-organizing network module; The satellite communication subsystem is used to provide wide-area communication via a low-Earth orbit satellite constellation; The drone swarm node is equipped with a dual-frequency communication module for simultaneous access to streetlights and satellite networks; A dynamic collaborative control platform is used to integrate street light sensor data and satellite positioning data in real time to generate cluster task instructions; The multi-mode communication gateway has an embedded protocol converter for bidirectional conversion between the satellite-specific space data packet protocol and the IEEE 802.15.4 protocol of the street light network. The environmental sensor array includes a lidar, a temperature and humidity sensor, and a people density camera. The data collected by the environmental sensor array is used by edge computing nodes to generate a gridded environmental risk map. The dual-frequency communication module integrates a link switching logic unit, which automatically connects to the triangular communication cluster formed by the three nearest streetlights when the satellite signal strength is lower than the threshold X. The construction method of the triangular communication cluster includes: screening the 10 candidate streetlights with the strongest signals, calculating the area of ​​the triangle formed by every three streetlights, with an area threshold greater than 100 square meters, and selecting the triangle with the largest sum of signal strength; The communication topology of the self-organizing network module adopts a time-division frequency-division multiple access mechanism to allocate dynamic time slots for the UAV swarm: in, For frame period, For the number of drones, Weighting coefficients for task priorities; Allocation rules: (1) Each UAV acquires a basic time slot: , in, Indicates the number of drones in the current communication domain; (2) Increase time slot resources by multiplying according to task priority coefficient. Specifically, time slot resources are multiplied using the following formula: in, For the original time slot, For the doubled time slot, This is the task priority coefficient. In addition to bandwidth utilization factors, a collision prevention mechanism was also set up: When there are too many high-priority tasks, that is Time: Activate excess factor Actual allocation: ; (3) Idle time slots are dynamically allocated to high-priority tasks; Specifically, the proposed scheme for environmental risk map modeling includes: Design a risk value calculation model: in: The dynamic risk value at target node j; Obstacle density; Crowd density; Temperature gradient; , , Weighting coefficient.

2. The UAV self-organizing network cluster management system based on smart streetlights and satellite communication according to claim 1, characterized in that, The dynamic calculation method for the threshold X is as follows: in, For minimum receiving power, Noise figure Boltzmann's constant, Absolute temperature For bandwidth.

3. The UAV self-organizing network cluster management system based on smart streetlights and satellite communication according to claim 1, characterized in that, The dynamic collaborative control platform includes: The task assignment engine divides drones into task groups based on a risk map.

4. The UAV self-organizing network cluster management system based on smart streetlights and satellite communication according to claim 1, characterized in that, The dynamic collaborative control platform includes: The path dynamic optimizer recalculates the cluster path at preset intervals to avoid high-risk areas.

5. The UAV self-organizing network cluster management system based on smart streetlights and satellite communication according to claim 1, characterized in that, The system also includes an emergency relay drone, which stores and forwards critical instructions using a delay-tolerant network module carried by the emergency relay drone when both the satellite and street light networks fail simultaneously.

6. The UAV self-organizing network cluster management system based on smart streetlights and satellite communication according to claim 5, characterized in that, The delay-tolerant network module employs an infection routing algorithm, and the message replica diffusion formula is: in, Maximum number of hops, TTL is time to live. This is the preset maximum number of replicas.

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