Unmanned aerial vehicle control system and method for Internet of Things communication

Abstract

The invention discloses an unmanned aerial vehicle control system and method for Internet of Things communication, and relates to the technical field of unmanned aerial vehicle control, the unmanned aerial vehicle control system comprises a data acquisition module, a decision processing module and the like, a switching instruction is generated through multi-dimensional data fusion, cross-layer collaboration, dynamic spectrum scheduling and three-level link guarantee are realized, and the unmanned aerial vehicle control system and method are applied to unmanned aerial vehicle control. The system has the advantages that through a multi-dimensional data fusion algorithm and a cross-layer cooperation mechanism, link quality, flight state and task type data are acquired in real time by using the data acquisition module, and a precise network switching instruction is generated through the decision processing module; in cooperation with a pre-registered address caching mechanism of the cross-layer cooperation module and a dynamic spectrum allocation strategy of the spectrum scheduling module, the defects of heterogeneous network switching decision lag, cross-layer protocol conflict and dynamic spectrum adaptation deficiency in the prior art are effectively solved, and the scheme realizes intelligent seamless switching of the unmanned aerial vehicle between heterogeneous networks such as a cellular network and a satellite network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle control system and method for Internet of Things communication. BACKGROUND

[0002] With the rapid development of Internet of Things technology, unmanned aerial vehicles, as an important carrier of mobile intelligent terminals in the Internet of Things system, are increasingly widely used in smart city management, emergency rescue, agricultural plant protection, logistics distribution and other fields. The unmanned aerial vehicle control system needs to realize the whole-process management and control of flight attitude, task execution and data transmission. The integration of Internet of Things communication technology provides ubiquitous connectivity and data-driven foundation.

[0003] The prior art has certain defects. First, the heterogeneous network switching decision in the prior art relies on a single parameter, lacks cross-layer protocol cooperation, and lacks dynamic spectrum adaptation, resulting in switching lag and high interruption rate. Second, the spectrum resource allocation in the prior art is static and inefficient, and lacks anti-interference mechanism, resulting in low spectrum utilization and poor communication stability in complex electromagnetic environments. Therefore, we propose an unmanned aerial vehicle control system and method for Internet of Things communication. SUMMARY

[0004] The purpose of the present application is to provide an unmanned aerial vehicle control system and method for Internet of Things communication.

[0005] To achieve the above purpose, the present application provides the following technical solution: an unmanned aerial vehicle control system for Internet of Things communication, the control system comprising the following modules:

[0006] A data acquisition module configured to acquire link quality data of a cellular network, a satellite communication network and an ad hoc network currently connected to the unmanned aerial vehicle, as well as flight speed, heading angle change rate, flight state data and task type data of the unmanned aerial vehicle in real time;

[0007] A decision processing module in communication connection with the data acquisition module, configured to generate a network switching trigger instruction based on the link quality data, flight speed, heading angle change rate, flight state data and task type data;

[0008] A cross-layer cooperation module in communication connection with the decision processing module, configured to send a pre-registration request to the data link layer and pre-allocate a target network address segment to the network layer after receiving the network switching trigger instruction;

[0009] A spectrum scheduling module in communication connection with the cross-layer cooperation module, configured to dynamically allocate available frequency bands according to real-time spectrum detection results;

[0010] Link management module: in communication connection with spectrum scheduling module, configured to preferentially select 5G cellular network transmission for high-definition video data in main link, real-time running satellite communication link in backup link to synchronize flight state data, standby based on low-power ad hoc network in emergency link;

[0011] Instruction transmission module: in communication connection with link management module and unmanned aerial vehicle flight control system respectively, configured to transmit control instructions through main link and backup link, and automatically switch to emergency link to transmit emergency control instructions when main link and backup link are invalid;

[0012] Storage module: in communication connection with data acquisition module and decision processing module, configured to store historical link quality data, flight state data and task execution data.

[0013] As a further scheme of the application: the link quality data collected by the data acquisition module includes signal receiving power, bit error rate and signal-to-noise ratio, the flight state data includes flight speed vector, acceleration and height change rate, and the task type data corresponds to generate a task priority coefficient of 0-1.

[0014] As a further scheme of the application: the decision processing module includes a fuzzy logic processing unit, configured to normalize the link quality data, flight state data and task priority coefficient, generate a network switching trigger probability through a weighted fusion algorithm, output a switching instruction when the trigger probability exceeds a preset threshold, and the calculation formula of the weighted fusion algorithm is as follows:

[0015] ;

[0016] Wherein, is the network switching trigger probability, is the first link quality index, is the link quality index weight, is the first flight state index, is the flight state index weight, is the task priority coefficient, is the task priority correction factor, is the number of link quality indexes, is the number of flight state indexes. As a further scheme of the application: the cross-layer cooperation module includes a pre-registration address cache unit, configured to apply for reserving a continuous IP address segment in advance to the network layer and store it in the cache unit when the data link layer detects that the link bit error rate exceeds the early warning threshold.

[0017] As a further scheme of the application: the cross-layer cooperation module includes a pre-registration address cache unit, configured to apply for reserving a continuous IP address segment in advance to the network layer and store it in the cache unit when the data link layer detects that the link bit error rate exceeds the early warning threshold.

[0018] ​As a further scheme of the present application: the spectrum scheduling module comprises a SDR unit of software defined radio and a reinforcement learning algorithm unit, the SDR unit is configured to scan the available frequency spectrum range once every 50 ms, and the reinforcement learning algorithm unit generates a dynamic spectrum allocation strategy according to historical spectrum usage data;

[0019] The reinforcement learning algorithm unit adopts a state-action-reward model, defines a state space as a current spectrum occupation matrix , is the number of frequency bands, is the number of time slots of spectrum detection, and an action space is a spectrum allocation vector , represents whether the th frequency band is allocated, and a reward function is as follows:

[0020] ;

[0021] wherein, represents a reward function of spectrum allocation action, is a spectrum utilization rate, is a co-channel interference index, is a data transmission delay, 、 and are weight coefficients, and the reward function is optimized by a deep Q network training to generate an optimal spectrum allocation strategy.

[0022] As a further scheme of the present application: the link management module is configured to keep real-time data synchronization between the main link and the backup link, the synchronization interval is not more than 100-150 ms, and the emergency link is automatically activated when the signal strength of the main link and the backup link is lower than a receiving threshold;

[0023] The emergency link adopts a LoRa communication protocol and is configured to only transmit emergency control data containing unmanned aerial vehicle position coordinates, power information and return instructions, and the data amount of a single transmission is not more than 128 bytes.

[0024] As a further scheme of the present application: the storage module comprises a time series database unit configured to store link quality data, flight state data and task execution data in chronological order, and support historical data classification retrieval based on task type.

[0025] As a further scheme of the present application: the instruction transmission module is configured to perform hierarchical encryption on control instructions, the emergency control instruction adopts a lightweight symmetric encryption algorithm, and the encryption processing rate is not less than 50 Mbps, and the regular control instruction adopts an asymmetric encryption algorithm for integrity verification.

[0026] In addition, the application also provides a UAV control method for Internet of Things communication, the control method comprising the following steps:

[0027] Step one, real-time collection of link quality data of cellular networks, satellite communication networks and ad hoc networks currently connected by the UAV, and flight speed, heading angle change rate and task type data of the UAV, generation of a task priority coefficient;

[0028] Step two, based on the collected link quality data, flight state data and task priority coefficient, calculation of a network switching trigger probability by a multi-dimensional data fusion algorithm, and generation of a network switching trigger instruction when the probability exceeds a threshold value;

[0029] Step three, after receiving the switching trigger instruction, sending a pre-registration request to the data link layer and pre-allocating a target network address segment to the network layer, simultaneously, scanning available frequency spectrum by a software-defined radio of the spectrum scheduling module, and dynamically allocating frequency bands by using a reinforcement learning algorithm to optimize frequency spectrum utilization and anti-interference capability;

[0030] Step four, establishment of a three-level protection system of a main link, a backup link and an emergency link, transmission of high-definition video by the main link, real-time synchronization of flight data by the backup link, and automatic switching to the emergency link for transmission of emergency instructions when the main and backup links fail;

[0031] Step five, hierarchical encryption of control instructions, and storage of historical link quality, flight state and task data according to time stamps, and support of classification retrieval based on task types.

[0032] Compared with the prior art, the application has the following beneficial effects:

[0033] 1. By using the multi-dimensional data fusion algorithm and the cross-layer collaborative mechanism, the data acquisition module is used to acquire link quality, flight state and task type data in real time, the decision processing module is used to generate accurate network switching instructions, the pre-registration address caching mechanism of the cross-layer collaborative module is used, and the dynamic spectrum allocation strategy of the spectrum scheduling module is used, so that the defects of decision lag, cross-layer protocol conflict and lack of dynamic spectrum adaptation in the prior art are effectively solved, the intelligent seamless switching of the UAV between heterogeneous networks such as cellular networks and satellite networks is realized, the real-time performance and reliability of the communication link are improved, the communication industry standard for civil UAVs is met, stable air-ground collaborative control capability is provided for complex scenarios such as smart cities and emergency rescue, and the adaptability of the UAV control system in the Internet of Things communication environment is enhanced;

[0034] 2、The present application cooperates the software defined radio unit of the spectrum scheduling module with the reinforcement learning algorithm unit, scans the available spectrum in real time and dynamically allocates the frequency band based on the state-action-reward model, combines the three-level protection system of the main link-backup link-emergency link of the link management module, effectively solves the defects of low efficiency of spectrum resource allocation and weak anti-interference ability in the prior art, realizes intelligent optimization configuration of spectrum resources, significantly improves spectrum utilization and anti-interference performance, ensures the stability of the communication link of the unmanned aerial vehicle in the complex electromagnetic environment, provides support for reliable control in the multi-machine cooperation and dense spectrum scene, and enhances the spectrum adaptation ability and task continuity of the unmanned aerial vehicle control system in the Internet of Things communication environment. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is an intelligent network switching flowchart in the embodiment of the present application.

[0036] Figure 2 It is a system flowchart in the embodiment of the present application. DETAILED DESCRIPTION

[0037] The specific embodiments of the present application will be further described below in conjunction with the drawings, and it should be noted that the description of these embodiments is used to help understand the present application and does not constitute a limitation on the present application.

[0038] In addition, the technical features involved in each of the embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0039] Please refer to the accompanying Figure 1 -Appendix Figure 2 The present application is a kind of unmanned aerial vehicle control system for Internet of Things communication, and the control system comprises the following modules:

[0040] Data acquisition module: configured to acquire the link quality data of the cellular network, satellite communication network and ad hoc network currently connected to the unmanned aerial vehicle in real time, and the flight speed, heading angle change rate, flight state data and task type data of the unmanned aerial vehicle;

[0041] Decision processing module: in communication connection with the data acquisition module, configured to generate network switching trigger instruction based on the link quality data, flight speed, heading angle change rate, flight state data and task type data;

[0042] Cross-layer coordination module: in communication connection with the decision processing module, configured to send a pre-registration request to the data link layer and pre-allocate a target network address segment to the network layer after receiving the network switching trigger instruction;

[0043] Spectrum scheduling module: in communication connection with the cross-layer coordination module, configured to dynamically allocate available frequency band according to real-time spectrum detection results;

[0044] Link Management Module: Communicates with the spectrum scheduling module, is configured to prioritize the use of 5G cellular network to transmit high-definition video data for the primary link, run satellite communication links in real time to synchronize flight status data for the backup link, and keep the emergency link on standby based on a low-power self-organizing network;

[0045] Command transmission module: It communicates with the link management module and the UAV flight control system respectively, and is configured to transmit control commands through the main link and backup link. When the main link and backup link fail, it automatically switches to the emergency link to transmit emergency control commands.

[0046] Storage module: Communicates with the data acquisition module and decision processing module, and is configured to store historical link quality data, flight status data, and mission execution data;

[0047] The decision processing module generates network switching trigger commands based on multi-dimensional data fusion algorithms, the cross-layer collaboration module realizes signaling interaction between the data link layer and the network layer, and the link management module establishes a three-level protection system of main link, backup link, and emergency link.

[0048] In one embodiment of the present invention: the link quality data collected by the data acquisition module includes signal received power, bit error rate and signal-to-noise ratio; the flight status data includes flight speed vector, acceleration and altitude change rate; and the mission type data generates a mission priority coefficient of 0-1.

[0049] In one embodiment of the present invention: the decision processing module includes a fuzzy logic processing unit, configured to normalize link quality data, flight status data, and task priority coefficients, generate a network handover trigger probability through a weighted fusion algorithm, and output a handover command when the trigger probability exceeds a preset threshold. The calculation formula of the weighted fusion algorithm is as follows:

[0050] ;

[0051] in, This represents the probability of network handover being triggered. For the first Each link quality indicator As the weight of the link quality metric, For the first One flight status indicator, As the weight of flight status indicators, This is the task priority coefficient. This is a task priority correction factor. This refers to the number of link quality metrics. This refers to the number of flight status indicators.

[0052] In one embodiment of the present invention: the cross-layer collaboration module includes a pre-registered address caching unit, configured to request a reserved continuous IP address range from the network layer and store it in the caching unit when the data link layer detects that the link error rate exceeds the warning threshold.

[0053] In one embodiment of the present invention: the spectrum scheduling module includes a software-defined radio SDR unit and a reinforcement learning algorithm unit. The SDR unit is configured to scan the available spectrum range every 50ms, and the reinforcement learning algorithm unit generates a dynamic spectrum allocation strategy based on historical spectrum usage data.

[0054] The reinforcement learning algorithm unit adopts a state-action-reward model, defining the state space as the current spectrum occupancy matrix. , For the number of frequency bands, The time slots for spectrum detection are denoted by , and the action space is the spectrum allocation vector. , Indicates the first (Whether the frequency band is allocated), the reward function is as follows:

[0055] ;

[0056] in, The reward function represents the spectrum allocation action (negative rewards trigger policy adjustment, and the algorithm automatically switches to the suboptimal frequency band). For spectrum utilization, This refers to the co-channel interference index. For data transmission delay, , and The weights are used to optimize the reward function through training a deep Q-network to generate the optimal spectrum allocation strategy.

[0057] In one embodiment of the present invention: the link management module is configured to maintain real-time data synchronization between the primary link and the backup link, with a synchronization interval not exceeding 100ms-150ms; the emergency link is automatically activated when the signal strength of both the primary link and the backup link is lower than the reception threshold.

[0058] The emergency link uses the LoRa communication protocol and is configured to transmit only emergency control data, including the drone's location coordinates, battery information, and return-to-home command. The amount of data transmitted in a single transmission does not exceed 128 bytes.

[0059] In one embodiment of the present invention, the storage module includes a time-series database unit configured to store link quality data, flight status data and mission execution data in timestamp order, and supports historical data classification retrieval based on mission type.

[0060] In one embodiment of the present application: the instruction transmission module is configured to perform hierarchical encryption on the control instructions, the emergency control instructions use a lightweight symmetric encryption algorithm, and the encryption processing rate is not less than 50 Mbps, and the regular control instructions use an asymmetric encryption algorithm for integrity verification.

[0061] Embodiment one, please refer to the attached Figure 1 -attached Figure 2 , unmanned aerial vehicle control scene based on smart city monitoring:

[0062] I. Background

[0063] In a certain city's intelligent traffic management system, the unmanned aerial vehicle needs to transmit road monitoring video in real time and receive traffic dispatching instructions, the flight height is 100-150 meters, the speed is 60-80 km / h, and the communication environment includes 5G cellular network (frequency band 3.5 GHz), urban Wi-Fi ad hoc network (2.4 GHz), and backup Beidou satellite link;

[0064] II. Specific parameters of each module

[0065] Data acquisition module

[0066] Link quality data:

[0067] 5G link: signal receiving power -75dBm, bit error rate 5×10 -5 , signal-to-noise ratio 25dB;

[0068] Wi-Fi link: signal receiving power -80dBm, bit error rate 1×10 -4 , signal-to-noise ratio 20dB;

[0069] Flight state data:

[0070] Flight speed vector (15m / s, horizontal direction), acceleration 0.5m / s 2 , height change rate 2m / s;

[0071] Task type data: regular monitoring task, generate task priority coefficient =0.5.

[0072] Decision processing module

[0073] Weighted fusion algorithm parameters:

[0074] Number of link quality indicators =3 (signal power, bit error rate, signal-to-noise ratio), corresponding weights =0.4, =0.3, =0.3;

[0075] Number of flight state indicators n = 3 (speed, acceleration, rate of change of altitude), corresponding weights = 0.3, = 0.4, = 0.3;

[0076] Task priority correction factor = 1.0;

[0077] Trigger probability calculation:

[0078] (threshold) (Note: each indicator has been normalized to [0, 1]), no trigger network switching.

[0079] Cross-layer coordination module

[0080] Pre-registration address cache unit: when the 5G link error rate exceeds the warning threshold 10 -3 When the 5G link error rate exceeds the warning threshold 10 -3 , 16 consecutive IP address segments of 192.168.1.100-192.168.1.115 are applied to the network layer in advance;

[0081] Spectrum scheduling module

[0082] SDR unit: scan 2.4GHz-5.8GHz frequency range every 50ms, detect 2.4GHz band interference index 0.6 (0-1), 5.8GHz band is idle;

[0083] Reinforcement learning algorithm parameters:

[0084] State space: K = 10 frequency bands, N = 10 time slots, the corresponding element S 5,5 = 0 (idle) in the 5.8GHz frequency band in the spectrum occupation matrix S;

[0085] Reward function weights: = 0.5 (spectrum utilization), = 0.3 (anti-interference), = 0.2 (delay);

[0086] Reward value calculation:

[0087] Spectrum utilization = 0.9 (5.8GHz band is idle);

[0088] Co-channel interference index = 0.1 (no interference in the allocated frequency band);

[0089] Data transmission delay = 20ms (5.8GHz band measured value);

[0090] Allocation strategy: select 5.8GHz band ( =1), calculate the reward value:

[0091] ;

[0092] A negative number indicates that the allocation strategy needs to be optimized, and the algorithm will adjust the frequency band selection through DQN learning.

[0093] Link management module

[0094] Main link: 5G cellular network transmission of 1080P video (code rate 8Mbps);

[0095] Backup link: Beidou satellite link synchronizes flight status data, synchronization interval 100ms;

[0096] Emergency link: LoRa standby, signal strength threshold -100dBm.

[0097] Instruction transmission module

[0098] Regular control instructions (such as route adjustment) use RSA-2048 asymmetric encryption, with 256-bit checksum;

[0099] Video data uses H.264 encoding and is transmitted through the 5G link with a delay of ≤150ms.

[0100] III. Workflow

[0101] The UAV flies along the preset route, the data acquisition module real-time returns the 5G link quality and flight status data, the decision processing module calculates the switching probability, maintains the current link, and the spectrum scheduling module attempts to allocate the 5.8GHz frequency band due to the high DLT, resulting in a negative reward value. The algorithm automatically adjusts to allocate the 5.1GHz frequency band with lower interference (after correction Reward value R = 0.5 x 0.8 + 0.3 x 0.9 - 0.2 x 15 = 0.4 + 0.27 - 3 = -2.33, still negative, finally select 3.5GHz main link frequency band).

[0102] Example two, please refer to the attached Figure 1 -Appendix Figure 2 , UAV control application in emergency rescue scenarios:

[0103] I. Scenario background

[0104] In mountainous earthquake rescue, the UAV needs to pass through the canyon terrain, real-time transmission of disaster area pictures and reception of rescue instructions, flight height 50-100 meters, speed 120-150km / h, communication environment includes 4G cellular network (frequency band 2.1GHz) in damaged area, Mesh self-organizing network (433MHz) and Inmarsat satellite backup link.

[0105] II. Specific parameters of each module:

[0106] Data collection module

[0107] Link quality data:

[0108] 4G link: signal receiving power -95dBm, error rate 8x10 -4 , signal-to-noise ratio 15dB;

[0109] Mesh link: signal receiving power -85dBm, error rate 5x10 -4 , signal-to-noise ratio 18dB;

[0110] Flight state data:

[0111] Flight speed vector (35m / s, diving direction), acceleration 2m / s2, height change rate -5m / s;

[0112] Task type data: emergency rescue task, generate task priority coefficient =1.0;

[0113] Decision processing module

[0114] Weighted fusion algorithm parameters:

[0115] Link quality weight adjustment =0.3 (signal power), =0.4 (error rate), =0.3 (signal-to-noise ratio);

[0116] Flight state weight =0.4 (speed), =0.3 (acceleration), =0.3 (height change rate);

[0117] Task priority correction factor =1.2 (emergency scene enhancement);

[0118] Trigger probability calculation:

[0119] (Index normalized value: 4G signal power 0.6, error rate 0.4, signal-to-noise ratio 0.7; speed 0.9, acceleration 0.8, height change rate 0.7):

[0120] , trigger network switching instruction, switch from 4G link to Mesh ad hoc network;

[0121] Cross-layer coordination module

[0122] Pre-registration address cache unit: when 4G link error rate exceeds 10 -3When the network layer applies for 172.16.0.10-172.16.0.25, a total of 16 IP addresses, stored in the cache unit.

[0123] Spectrum scheduling module

[0124] SDR unit: scan 400MHz-480MHz frequency band every 50ms, detect 433MHz frequency band interference index 0.3;

[0125] Reinforcement learning parameter adjustment:

[0126] Weight coefficient =0.3, =0.6 (priority anti-interference), =0.1;

[0127] Allocation strategy: select 433MHz frequency band (a3=1), calculate reward value:

[0128] =0.8 (frequency band utilization rate);

[0129] =0.3 (interference index);

[0130] =30ms (Mesh link measured delay);

[0131] ;

[0132] (Note: In response to the emergency scene, link connectivity is prioritized. Even if the reward value is negative, the allocation is still executed, and the algorithm is optimized through subsequent iterations).

[0133] Link management module

[0134] Main link: Mesh ad hoc network transmits 720P video (code rate 4Mbps);

[0135] Backup link: Inmarsat satellite link synchronizes data, synchronization interval 150ms;

[0136] Emergency link: LoRa is activated, transmits coordinates (longitude 116.5°, latitude 39.8°, height 80m), power 30%, return instruction, data volume 120 bytes / time;

[0137] Instruction transmission module

[0138] Emergency control instructions (such as obstacle avoidance) use SM4 symmetric encryption, processing rate 50Mbps, encryption delay ≤10ms;

[0139] Satellite link transmission delay 200ms, reduced to 120ms after pre-caching address optimization.

[0140] III. Workflow

[0141] When the UAV enters the canyon area, the 4G link error rate rises to 8x10 -4 , the decision module triggers the switching instruction, the cross-layer cooperation module pre-allocates the Mesh network IP address, the spectrum scheduling module dynamically allocates the 433MHz frequency band, the emergency link transmits the emergency obstacle avoidance instruction during the switching process, ensures that the UAV avoids the mountain obstacles, the whole switching process takes 40ms, the communication is not interrupted, and the image back transmission in the disaster area is successfully completed.

[0142] According to the above embodiment, it can be concluded that by means of the real-time acquisition of multi-dimensional data by the data acquisition module, the switching instruction generated by the fusion algorithm of the decision processing module, the pre-registration address mechanism of the cross-layer cooperation module and the dynamic spectrum allocation strategy of the spectrum scheduling module, the defects of heterogeneous network switching decision lag and protocol conflict are effectively solved, the intelligent seamless switching of the UAV between the cellular network and the satellite network is realized, the communication real-time performance and reliability are improved, at the same time, with the cooperation of the SDR of the spectrum scheduling module and the reinforcement learning algorithm, combined with the three-level link protection system of the link management module, the defects of inefficient spectrum resource allocation and weak anti-interference performance are effectively solved, the intelligent optimization of spectrum and the improvement of anti-interference performance are realized, the communication stability in the complex electromagnetic environment is ensured, and finally the adaptability and task continuity of the UAV control system in the Internet of Things communication scene are enhanced.

[0143] Although the present application is disclosed in the preferred embodiments as above, it is not intended to limit the present application, and any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solutions of the present application, fall within the protection scope defined by the claims of the present application.

Claims

1. A drone control system for Internet of Things communication, characterized in that, The control system comprises the following modules: a data acquisition module configured to acquire link quality data of a cellular network, a satellite communication network and an ad hoc network currently connected by the unmanned aerial vehicle, and flight speed, heading angle change rate, flight state data and task type data of the unmanned aerial vehicle in real time; a decision processing module in communication connection with the data acquisition module and configured to generate a network switching trigger instruction based on the link quality data, the flight speed, the heading angle change rate, the flight state data and the task type data; a cross-layer cooperation module in communication connection with the decision processing module and configured to send a pre-registration request to a data link layer and pre-allocate a target network address segment to a network layer after receiving the network switching trigger instruction; a spectrum scheduling module in communication connection with the cross-layer cooperation module and configured to dynamically allocate available frequency bands according to real-time spectrum detection results; a link management module in communication connection with the spectrum scheduling module and configured to preferentially select the 5G cellular network for the main link to transmit high-definition video data, to run the satellite communication link in real time for the backup link to synchronize the flight state data, and to keep standby based on the low-power ad hoc network for the emergency link; an instruction transmission module in communication connection with the link management module and the flight control system of the unmanned aerial vehicle and configured to transmit control instructions through the main link and the backup link, and to automatically switch to the emergency link to transmit emergency control instructions when the main link and the backup link fail; a storage module in communication connection with the data acquisition module and the decision processing module and configured to store historical link quality data, flight state data and task execution data.

2. The UAV control system for IoT communications of claim 1, wherein: The link quality data acquired by the data acquisition module includes signal receiving power, bit error rate and signal-to-noise ratio, the flight state data includes flight speed vector, acceleration and height change rate, and the task type data corresponds to a task priority coefficient of 0-1.

3. The drone control system for Internet of Things communication of claim 2, wherein: The decision processing module comprises a fuzzy logic processing unit configured to normalize the link quality data, the flight state data and the task priority coefficient, to generate a network switching trigger probability through a weighted fusion algorithm, and to output a switching instruction when the trigger probability exceeds a preset threshold, and the calculation formula of the weighted fusion algorithm is as follows: ； wherein, is a network switching trigger probability, is a first link quality indicator, is a link quality indicator weight, is a first flight status indicator, is a flight status indicator weight, is a task priority coefficient, is a task priority correction factor, is a number of link quality indicators, is a number of flight status indicators.

4. The drone control system for IoT communication of claim 3, wherein: The cross-layer cooperation module comprises a pre-registration address cache unit configured to apply for reserving a continuous IP address segment in advance to the network layer and store it in the cache unit when the data link layer detects that the link bit error rate exceeds a warning threshold.

5. The drone control system for IoT communication of claim 1, wherein: The spectrum scheduling module comprises an SDR unit of a software-defined radio and a reinforcement learning algorithm unit, the SDR unit is configured to scan the available frequency spectrum range once every 50 ms, and the reinforcement learning algorithm unit generates a dynamic spectrum allocation strategy according to historical spectrum usage data; The reinforcement learning algorithm unit adopts a state-action-reward model, defines a state space as a current spectrum occupation matrix , is the number of frequency bands, is the number of time slots of spectrum detection, and an action space is a spectrum allocation vector , represents whether the th frequency band is allocated, and a reward function is as follows: ； wherein, a reward function representing the spectrum allocation action, is a spectrum utilization, is a co-channel interference index, is a data transmission delay, , and are weight coefficients, the reward function is optimized by a deep Q network training to generate an optimal spectrum allocation strategy.

6. The drone control system for Internet of Things communication of claim 1, wherein: The link management module is configured to keep the main link and the backup link in real-time data synchronization, the synchronization interval is not more than 100-150 ms, and the emergency link is automatically activated when the signal strength of the main link and the backup link is lower than a receiving threshold; The emergency link adopts a LoRa communication protocol and is configured to only transmit emergency control data containing the position coordinates of the unmanned aerial vehicle, power information and return instructions, and the amount of data transmitted at a time is not more than 128 bytes.

7. The drone control system for Internet of Things communication of claim 1, wherein: The storage module comprises a time sequence database unit configured to store link quality data, flight state data and task execution data in timestamp order and support historical data classification retrieval based on task type.

8. The drone control system for Internet of Things communication of claim 1, wherein: The instruction transmission module is configured to perform hierarchical encryption on the control instructions, with emergency control instructions using a lightweight symmetric encryption algorithm and an encryption processing rate of no less than 50 Mbps, and regular control instructions using an asymmetric encryption algorithm for integrity verification. 9.A control method suitable for the UAV control system for Internet of Things communication according to any one of claims 1-8, characterized in that: The control method comprises the following steps: Step one, real-time collection of link quality data of a cellular network, a satellite communication network and an ad hoc network currently connected by the UAV, and flight speed, heading angle change rate and task type data of the UAV, to generate a task priority coefficient; Step two, calculation of a network switching trigger probability based on the collected link quality data, flight state data and task priority coefficient through a multi-dimensional data fusion algorithm, and generation of a network switching trigger instruction when the probability exceeds a threshold value; Step three, after receiving the switching trigger instruction, sending a pre-registration request to the data link layer and pre-allocating a target network address segment to the network layer, simultaneously scanning available frequency spectrum through a software-defined radio of the spectrum scheduling module and dynamically allocating frequency bands using a reinforcement learning algorithm to optimize frequency spectrum utilization and anti-interference capability; Step four, establishment of a three-level protection system for a main link, a backup link and an emergency link, with the main link transmitting high-definition video, the backup link synchronizing flight data in real time, and automatic switching to the emergency link to transmit emergency instructions when the main and backup links fail; Step five, hierarchical encryption of the control instructions and timestamp storage of historical link quality, flight state and task data to support classification retrieval based on task type.

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