Unmanned aerial vehicle control system and method based on Internet of Things

Abstract

The invention discloses an unmanned aerial vehicle control system and method based on the Internet of Things, and relates to the technical field of data encryption, and the method comprises the steps: enabling an unmanned aerial vehicle to be connected with a central control platform through the Internet of Things, and enabling the unmanned aerial vehicle to receive a task demand; a preliminary flight path is generated according to a task weight, task start time, task end time and route length in a task demand, the unmanned aerial vehicle performs routing inspection according to the preliminary flight path, meanwhile, environment data of a target site are collected through a sensor and preprocessed, and the task demand is determined based on the preprocessed environment data and the task demand. And calculating a comprehensive priority score, dynamically adjusting a flight path, executing a task according to the dynamically adjusted flight path, monitoring a flight state in real time, detecting abnormality and triggering an emergency strategy.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control systems and Internet of Things application technologies, and in particular to an unmanned aerial vehicle control system and method based on the Internet of Things. Background Art

[0002] In recent years, with the rapid development of Internet of Things technology, its application areas have been continuously expanded to multiple industries such as industry, agriculture, logistics, and energy. In these fields, drones, as a highly flexible and efficient intelligent equipment, have been widely used due to their excellent performance in inspection, monitoring, distribution and other tasks. By carrying a variety of sensors and communicators, drones can perform a variety of tasks in complex environments, and combined with Internet of Things technology, remote monitoring, data collection and processing can be achieved. However, the existing drone control methods still have certain limitations in task management, path planning, status monitoring, etc., and it is difficult to give full play to the autonomous capabilities of drones in dynamic environments. Traditional drone control usually relies on preset flight paths and fixed task allocation strategies, and cannot dynamically adjust task execution plans according to real-time environmental changes, which may lead to resource waste, task delays and even flight accidents. When multiple drones perform tasks collaboratively, due to the lack of efficient task allocation and path optimization mechanisms, path conflicts or task redundancy problems are easily caused, affecting the overall efficiency of the task.

[0003] Existing UAV control technology also faces the challenge of insufficient environmental perception and emergency decision-making capabilities. Under complex and dynamic environmental conditions, UAVs often find it difficult to perceive in real time and quickly adjust flight parameters, resulting in flight path deviation or mission interruption. Existing technologies have a low level of intelligence in task priority sorting and path dynamic optimization. They usually simply rely on fixed task weights or manually set priorities, and fail to fully combine real-time environmental data for comprehensive weighing, resulting in a lack of flexibility and accuracy in task execution. Especially in scenarios with multiple overlapping tasks or complex task requirements, traditional systems find it difficult to weigh the time requirements, path lengths and environmental constraints of tasks, which may ultimately lead to important tasks being delayed or secondary tasks occupying too many resources. Existing systems mostly adopt a passive response mode for flight anomaly detection and emergency strategy triggering, lacking the ability to actively predict and dynamically avoid potential risks, further limiting the adaptability of UAVs in highly dynamic environments. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a drone control method based on the Internet of Things to solve the problem that the existing technology has a low level of intelligence in task priority sorting and path dynamic optimization. It usually simply relies on fixed weights or manually set priorities of tasks, and fails to fully combine real-time environmental data for comprehensive weighing, resulting in a lack of flexibility and accuracy in task execution.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for controlling a drone based on the Internet of Things, comprising:

[0008] The drone is connected to the central control platform through the Internet of Things. The drone receives the mission requirements and generates a preliminary flight path based on the mission weight, mission start time, mission end time, and route length in the mission requirements.

[0009] The drone conducts inspections along the preliminary flight path, and at the same time, collects environmental data of the target location through sensors and performs preprocessing;

[0010] Based on the pre-processed environmental data and mission requirements, the comprehensive priority score is calculated and the flight path is dynamically adjusted;

[0011] Execute missions according to dynamically adjusted flight paths, monitor flight status in real time, detect anomalies and trigger emergency strategies.

[0012] As a preferred solution of the UAV control method based on the Internet of Things of the present invention, wherein: the UAV is connected to the central control platform through the Internet of Things, the UAV receives the task requirements, and generates a preliminary flight path according to the task weight, task start time, task end time, and route length in the task requirements. The specific steps are:

[0013] The drone uses the Internet of Things to connect to the central control platform, and the central control platform sends mission requirements to the drone through the MQTT protocol;

[0014] Time processing technology is used to analyze task requirements and obtain a preliminary priority score, which is expressed as:

[0015]

[0016] Among them, i represents the task point number, P i represents the initial priority score of the i-th task point, W represents the task weight, T a Indicates the task end time, T b represents the mission start time, d represents the route length, and E represents the current remaining battery power of the drone;

[0017] Give each task a preliminary priority score Pi Arrange from largest to smallest to get the preliminary task order;

[0018] Based on the preliminary priority score P i , calculate the preliminary flight path L, the expression is:

[0019]

[0020] Where C represents the drag coefficient, min represents the minimization function, represents the average flight speed of the UAV, and n represents the total number of mission points in mission planning.

[0021] As a preferred solution of the Internet of Things-based UAV control method of the present invention, wherein: the UAV performs inspection according to the preliminary flight path, and at the same time, collects environmental data of the target location through sensors and performs preprocessing, the specific steps are:

[0022] Install air pressure sensors, temperature sensors, humidity sensors, wind speed sensors, light sensors, and current sensors on the drone to collect air pressure data, temperature data, humidity data, wind speed data, wind direction data, light intensity data, and battery current data;

[0023] The environmental data were denoised using a Kalman filter, outliers were detected using the three-sigma method, and the environmental data were normalized;

[0024] The preprocessed environmental data is integrated into the environmental data set, and the environmental data set is set to x. The expression is:

[0025] x={x 1 、x 2 、x 3 、x 4 、x 5};

[0026] where x 1 Represents air pressure data, x 2 Represents wind speed data, x 3 Indicates wind direction data, x 4 Represents light intensity data, x 5 Indicates battery current data.

[0027] As a preferred solution of the IoT-based UAV control method of the present invention, the specific steps of calculating the comprehensive priority score based on the pre-processed environmental data and task requirements and dynamically adjusting the flight path are as follows:

[0028] Calculate the environmental impact factor S, the expression is:

[0029]

[0030] Among them, β 1 represents the air pressure weight coefficient, β 2 represents the wind speed weight coefficient, β 3 represents the wind direction weight coefficient, β 4 Represents the light intensity weight coefficient, β 5 represents the battery current weight coefficient, k represents the light response coefficient, x 4,c represents the light intensity threshold, e represents the base of the natural logarithm, and cos represents the cosine function;

[0031] Combined with the preliminary priority score P i , the preliminary optimal path L and the environmental impact factor S are used to calculate the dynamically adjusted comprehensive priority score, which is expressed as:

[0032]

[0033] Among them, P i ′ represents the comprehensive priority score of the i-th task point, γ represents the path balance factor, and L max Indicates the maximum path length allowed in task planning;

[0034] Among them, the path balance factor is obtained through historical data, and the expression is:

[0035]

[0036] Among them, P ′ i，o represents the historical comprehensive priority score, P i，o represents the historical preliminary priority score, S o represents the historical environmental impact factor, L o represents the historical preliminary flight path, L max，o Indicates the maximum path length allowed in historical mission planning.

[0037] The comprehensive priority score P of each task i ′ Arrange from largest to smallest to get the order of tasks;

[0038] Combined with the comprehensive priority score P i ′ , optimize the final path L ′ .

[0039] As a preferred solution of the method for controlling unmanned aerial vehicles based on the Internet of Things described in the present invention, wherein: the combined comprehensive priority score P ′ , optimize the final path L ′ , the expression is:

[0040]

[0041] Among them, λ represents the path optimization weight parameter, v i represents the flight speed of the UAV at the i-th mission point, E i Indicates the remaining power of the i-th task point;

[0042] The drone follows the final path L ′ Flight mission requirements.

[0043] As a preferred solution of the method for controlling a drone based on the Internet of Things described in the present invention, the specific steps of executing tasks according to the dynamically adjusted flight path, monitoring the flight status in real time, detecting anomalies and triggering emergency strategies are as follows:

[0044] The central control platform sends the best mission planning plan to the drone through the Internet of Things. The drone executes the mission according to the best mission planning plan and monitors the key flight parameters such as flight altitude, flight speed, remaining power, acceleration, ambient wind speed, and path deviation in real time.

[0045] The flight status score F is obtained through the flight status monitoring function status , the expression is:

[0046]

[0047] Where, h represents the flight altitude, h ref represents the ideal flight altitude, v represents the flight speed, and v ref Indicates the ideal flight speed. represents acceleration, It represents the maximum acceleration value that the drone can withstand, k represents the power response coefficient, E min represents the minimum remaining power threshold, f represents the path deviation, and f max represents the maximum path deviation;

[0048] F status The value range of is [0, +∞];

[0049] Set the threshold T 1 and T 2 , when F status ≤T 1 When it is on, it indicates that the flight status is normal and the mission can continue.

[0050] When T 1 <F status <T 2 When it is on, it means the flight status is slightly abnormal and the parameters need to be adjusted;

[0051] When T 2 ≤Fstatus , it indicates that the flight status is seriously abnormal and the emergency strategy is triggered.

[0052] As a preferred solution of the method for controlling a drone based on the Internet of Things described in the present invention, the specific steps of triggering the emergency strategy are as follows:

[0053] When T 2 ≤F status When , the abnormal detection phase is entered, and the source of the problem is located through the fault location function. The expression is:

[0054]

[0055] Among them, F cause represents the abnormal cause location function, k ′ represents the wind speed response coefficient, w represents the ambient wind speed, and w crit Indicates the critical value of ambient wind speed;

[0056] Set the threshold T 3 , when T 3 ≤F cause When , it means the fault is minor, and the task path can be replanned to continue the task;

[0057] When T 3 >F cause If the error message appears, it indicates a serious fault and the aircraft will immediately land in the nearest safe area, stop the mission and upload the abnormal status to the central control platform.

[0058] In a second aspect, the present invention provides a drone control system based on the Internet of Things, comprising:

[0059] Central control platform module: As the command center of the system, it is responsible for receiving mission requirements, generating preliminary mission planning solutions, and coordinating the work of various subsystems;

[0060] Communication network module: Use the Internet of Things technology to achieve data transmission between the drone and the central control platform to ensure the real-time and accuracy of information;

[0061] Environmental perception module: collects data about the surrounding environment through various sensors installed on the drone to provide raw data for subsequent processing;

[0062] Data preprocessing module: performs preliminary processing such as cleaning and formatting on the data collected from the environment to improve data quality and availability;

[0063] Intelligent decision support module: Combines pre-processed environmental data with current task requirements and uses algorithm models to dynamically adjust task planning plans to optimize execution efficiency;

[0064] Flight control module: guides the UAV to fly automatically according to the final mission planning plan, and also has obstacle avoidance function;

[0065] Status monitoring module: Continuously monitors the operating status and health of the drone, and can respond promptly and take corresponding measures when abnormalities are found;

[0066] Emergency response module: When encountering an emergency or receiving an abnormal signal, the emergency plan is quickly activated to ensure the safe landing of the drone or other necessary safety operations.

[0067] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the IoT-based drone control method as described in the first aspect of the present invention is implemented.

[0068] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for controlling a drone based on the Internet of Things as described in the first aspect of the present invention is implemented.

[0069] The beneficial effects of the present invention are as follows: the present invention realizes the intelligent scheduling of UAV tasks, dynamic optimization of path planning and precise adaptability to complex environments, especially combined with real-time communication of Internet of Things technology, efficient processing of environmental data and dynamic adjustment of task priorities. The patented method can effectively solve the problems of inefficient paths, unreasonable task allocation and poor environmental adaptability existing in traditional UAV control methods, and significantly improve the application value of UAVs in complex mission scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0071] Figure 1 This is a flow chart of the drone control method based on the Internet of Things in Example 1.

[0072] Figure 2 This is a system diagram of the IoT-based drone control system in Example 1. DETAILED DESCRIPTION

[0073] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0074] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0075] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0076] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for controlling a drone based on the Internet of Things, comprising the following steps:

[0077] The drone is connected to the central control platform through the Internet of Things. The drone receives the mission requirements and generates a preliminary flight path based on the mission weight, mission start time, mission end time, and route length in the mission requirements.

[0078] The drone conducts inspections along the preliminary flight path, and at the same time, collects environmental data of the target location through sensors and performs preprocessing;

[0079] Based on the pre-processed environmental data and mission requirements, the comprehensive priority score is calculated and the flight path is dynamically adjusted;

[0080] Execute missions according to dynamically adjusted flight paths, monitor flight status in real time, detect anomalies and trigger emergency strategies.

[0081] This embodiment also provides a drone control system based on the Internet of Things, including:

[0082] S1 connects the drone to the central control platform through the Internet of Things. The drone receives the mission requirements and generates a preliminary flight path based on the mission weight, mission start time, mission end time, and route length in the mission requirements.

[0083] The drone uses the Internet of Things to connect to the central control platform, and the central control platform sends mission requirements to the drone through the MQTT protocol;

[0084] Time processing technology is used to analyze task requirements and obtain a preliminary priority score, which is expressed as:

[0085]

[0086] Among them, i represents the task point number, Pi represents the initial priority score of the i-th task point, W represents the task weight, T a Indicates the task end time, T b represents the mission start time, d represents the route length, and E represents the current remaining battery power of the drone;

[0087] Drones can reasonably arrange the order of tasks according to task weights and time requirements, avoiding resource waste and inefficient path planning, thereby significantly improving task completion efficiency and reducing energy consumption during task execution;

[0088] Give each task a preliminary priority score P i Arrange from largest to smallest to get the preliminary task order;

[0089] Based on the preliminary priority score P i , calculate the preliminary flight path L, the expression is:

[0090]

[0091] Where C represents the drag coefficient, min represents the minimization function, represents the average flight speed of the UAV, and n represents the total number of mission points in the mission planning;

[0092] Through this step, the real-time connection between the UAV and the central control platform is realized, and the scientificity and efficiency of task allocation are ensured.

[0093] The S2 drone conducts inspections according to the preliminary flight path. At the same time, it collects environmental data of the target location through sensors and performs preprocessing;

[0094] Install air pressure sensors, temperature sensors, humidity sensors, wind speed sensors, light sensors, and current sensors on the drone to collect air pressure data, temperature data, humidity data, wind speed data, wind direction data, light intensity data, and battery current data;

[0095] Through real-time collection and preprocessing of environmental data, drones can fully perceive the dynamic changes in their environment and provide accurate decision-making basis for path adjustment and mission execution;

[0096] The environmental data were denoised using a Kalman filter, outliers were detected using the three-sigma method, and the environmental data were normalized;

[0097] The use of Kalman filter reduces the interference of sensor noise, and the normalization process improves the efficiency and applicability of multi-dimensional data integration, thereby ensuring the accuracy and consistency of environmental data;

[0098] The preprocessed environmental data is integrated into the environmental data set, and the environmental data set is set to x. The expression is:

[0099] x={x 1 、x 2 、x 3 、x 4 、x 5};

[0100] where x 1 Represents air pressure data, x 2 Represents wind speed data, x 3 Indicates wind direction data, x 4 Represents light intensity data, x 5 Indicates battery current data;

[0101] Through this step, the UAV can achieve comprehensive perception of the mission execution environment and generate high-quality environmental data sets, providing a scientific basis for subsequent task priority scoring and path adjustment. This capability enables the UAV to flexibly respond to environmental changes in complex environments and significantly improve the reliability and accuracy of mission completion.

[0102] S3 calculates the comprehensive priority score based on the pre-processed environmental data and mission requirements, and dynamically adjusts the flight path;

[0103] Calculate the environmental impact factor S, the expression is:

[0104]

[0105] Among them, β 1 represents the air pressure weight coefficient, β 2 represents the wind speed weight coefficient, β 3 represents the wind direction weight coefficient, β 4 Represents the light intensity weight coefficient, β 5 represents the battery current weight coefficient, k represents the light response coefficient, x 4,c represents the light intensity threshold, e represents the base of the natural logarithm, and cos represents the cosine function;

[0106] Combined with the preliminary priority score P i , the preliminary optimal path L and the environmental impact factor S are used to calculate the dynamically adjusted comprehensive priority score, which is expressed as:

[0107]

[0108] Among them, P i ′ represents the comprehensive priority score of the i-th task point, γ represents the path balance factor, and L max Indicates the maximum path length allowed in task planning;

[0109] Among them, the path balance factor is obtained through historical data, and the expression is:

[0110]

[0111] Among them, P ′ i，o represents the historical comprehensive priority score, P i，o represents the historical preliminary priority score, S o represents the historical environmental impact factor, L o represents the historical preliminary flight path, L max，o Indicates the maximum path length allowed in historical mission planning.

[0112] The comprehensive priority score P of each task i ′ Arrange from largest to smallest to get the order of tasks;

[0113] Combined with the comprehensive priority score P ′ , optimize the final path L ′ , the expression is:

[0114]

[0115] Among them, λ represents the path optimization weight parameter, v i represents the flight speed of the UAV at the i-th mission point, E i Indicates the remaining power of the i-th task point;

[0116] By optimizing the path L ′ , further reducing the energy consumption and time cost during task execution, while improving the overall efficiency of task completion;

[0117] The drone follows the final path L ′ Flight mission requirements;

[0118] This step enables the UAV to efficiently adapt to changes in complex environments based on real-time environmental data and mission requirements by dynamically adjusting mission priorities and flight paths;

[0119] This step significantly improves the intelligence level of the drone, enabling it to have the ability to dynamically adjust tasks and optimize paths. By comprehensively considering task priorities, environmental impacts and path balance factors, the drone can efficiently complete complex tasks, which not only reduces resource waste, but also significantly improves the accuracy and reliability of task completion in a dynamic environment.

[0120] S4 executes the mission according to the dynamically adjusted flight path, monitors the flight status in real time, detects anomalies and triggers emergency strategies;

[0121] The central control platform sends the best mission planning plan to the drone through the Internet of Things. The drone executes the mission according to the best mission planning plan and monitors the key flight parameters such as flight altitude, flight speed, remaining power, acceleration, ambient wind speed, and path deviation in real time.

[0122] The flight status score F is obtained through the flight status monitoring function status , the expression is:

[0123]

[0124] Where, h represents the flight altitude, h ref represents the ideal flight altitude, v represents the flight speed, and v ref Indicates the ideal flight speed. represents acceleration, It represents the maximum acceleration value that the drone can withstand, k represents the power response coefficient, E min represents the minimum remaining power threshold, f represents the path deviation, and f max represents the maximum path deviation;

[0125] F status The value range of is [0, +∞];

[0126] Set the threshold T 1 and T 2 , when F status ≤T 1 When it is on, it indicates that the flight status is normal and the mission can continue.

[0127] When T 1 <F status <T 2 When it is on, it means the flight status is slightly abnormal and the parameters need to be adjusted;

[0128] When T 2 ≤F status When , it indicates that the flight status is seriously abnormal and the emergency strategy is triggered;

[0129] It realizes real-time monitoring and quantitative evaluation of the UAV flight status, provides a hierarchical management mechanism for anomaly detection, improves the adaptability of UAVs to dynamic environments and the stability of mission execution, ensures efficient completion of missions, and the proactiveness of the anomaly handling mechanism significantly reduces mission failure rates and system risks.

[0130] When T 2 ≤F status When , the abnormal detection phase is entered, and the source of the problem is located through the fault location function. The expression is:

[0131]

[0132] Among them, F cause represents the abnormal cause location function, k ′ represents the wind speed response coefficient, w represents the ambient wind speed, and w crit Indicates the critical value of ambient wind speed;

[0133] Set the threshold T 3 , when T 3 ≤F cause When , it means the fault is minor, and the task path can be replanned to continue the task;

[0134] When T 3 >F cause If the error message is displayed, it indicates a serious fault. The system will immediately land in the nearest safe area, stop the mission, and upload the abnormal status to the central control platform.

[0135] It provides accurate fault location function, quickly analyzes and quantifies the source of the problem, and through a hierarchical emergency response mechanism, completes the task as much as possible while ensuring the safety of the drone, improves the robustness of the system, and combines the data upload function of the central control platform to provide comprehensive data support for subsequent task optimization and system upgrades.

[0136] This embodiment also provides a computer device, which is suitable for the case of a drone control method based on the Internet of Things, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the drone control method based on the Internet of Things proposed in the above embodiment.

[0137] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0138] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for controlling a drone based on the Internet of Things as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0139] In summary, the present invention realizes the intelligent scheduling of UAV tasks, dynamic optimization of path planning and precise adaptability to complex environments, especially combining real-time communication of Internet of Things technology, efficient processing of environmental data and dynamic adjustment of task priorities. The method of this patent can effectively solve the problems of inefficient paths, unreasonable task allocation and poor environmental adaptability existing in traditional UAV control methods, and significantly improve the application value of UAVs in complex mission scenarios.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A drone control method based on the Internet of Things, characterized in that: include, The drone is connected to the central control platform through the Internet of Things. The drone receives the mission requirements and generates a preliminary flight path based on the mission weight, mission start time, mission end time, and route length in the mission requirements. The drone conducts inspections along the preliminary flight path, and at the same time, collects environmental data of the target location through sensors and performs preprocessing; Based on the pre-processed environmental data and mission requirements, the comprehensive priority score is calculated and the flight path is dynamically adjusted; Execute missions according to dynamically adjusted flight paths, monitor flight status in real time, detect anomalies and trigger emergency strategies.

2. The method for controlling a drone based on the Internet of Things according to claim 1, characterized in that: The UAV is connected to the central control platform through the Internet of Things. The UAV receives the task requirements and generates a preliminary flight path according to the task weight, task start time, task end time, and route length in the task requirements. The specific steps are as follows: The drone uses the Internet of Things to connect to the central control platform, and the central control platform sends mission requirements to the drone through the MQTT protocol; Time processing technology is used to analyze task requirements and obtain a preliminary priority score, which is expressed as: Among them, i represents the task point number, P i represents the initial priority score of the i-th task point, W represents the task weight, T a Indicates the task end time, T b represents the mission start time, d represents the route length, and E represents the current remaining battery power of the drone; Give each task a preliminary priority score P i Arrange from largest to smallest to get the preliminary task order; Based on the preliminary priority score P i , calculate the preliminary flight path L, the expression is: Where C represents the drag coefficient, min represents the minimization function, v represents the average flight speed of the UAV, and n represents the total number of mission points in the mission planning.

3. The method for controlling a drone based on the Internet of Things as claimed in claim 2, characterized in that: The drone performs inspections according to the preliminary flight path, and at the same time, collects environmental data of the target location through sensors and performs preprocessing. The specific steps are as follows: Install air pressure sensors, temperature sensors, humidity sensors, wind speed sensors, light sensors, and current sensors on the drone to collect air pressure data, temperature data, humidity data, wind speed data, wind direction data, light intensity data, and battery current data; The environmental data were denoised using a Kalman filter, outliers were detected using the three-sigma method, and the environmental data were normalized; The preprocessed environmental data is integrated into the environmental data set, and the environmental data set is set to x. The expression is: x={x1,x2,x3,x4,x5}; Among them, x1 represents air pressure data, x2 represents wind speed data, x3 represents wind direction data, x4 represents light intensity data, and x5 represents battery current data.

4. The method for controlling a drone based on the Internet of Things as claimed in claim 3, characterized in that: The method of calculating the comprehensive priority score based on the pre-processed environmental data and mission requirements and dynamically adjusting the flight path includes the following specific steps: Calculate the environmental impact factor S, the expression is: Among them, β1 represents the air pressure weight coefficient, β2 represents the wind speed weight coefficient, β3 represents the wind direction weight coefficient, β4 represents the light intensity weight coefficient, β5 represents the battery current weight coefficient, k represents the light response coefficient, and x 4,c represents the light intensity threshold, e represents the base of the natural logarithm, and cos represents the cosine function; Combined with the preliminary priority score P i , the preliminary optimal path L and the environmental impact factor S are used to calculate the dynamically adjusted comprehensive priority score, which is expressed as: Among them, P i ′ represents the comprehensive priority score of the i-th task point, γ represents the path balance factor, and L max Indicates the maximum path length allowed in task planning; Among them, the path balance factor is obtained through historical data, and the expression is: Among them, P ′ i，o represents the historical comprehensive priority score, P i，o represents the historical preliminary priority score, S o represents the historical environmental impact factor, L o represents the historical preliminary flight path, L max，o Indicates the maximum path length allowed in historical mission planning. The comprehensive priority score P of each task i ′ Arrange from largest to smallest to get the order of tasks; Combined with the comprehensive priority score P i ′ , optimize the final path L ′ .

5. The method for controlling a drone based on the Internet of Things according to claim 4, characterized in that: The combined comprehensive priority score P ′ , optimize the final path L ′ , the expression is: Among them, λ represents the path optimization weight parameter, v i represents the flight speed of the UAV at the i-th mission point, E i Indicates the remaining power of the i-th task point; The drone follows the final path L ′ Flight mission requirements.

6. The method for controlling a drone based on the Internet of Things according to claim 5, characterized in that: The specific steps of executing the mission according to the dynamically adjusted flight path, monitoring the flight status in real time, detecting anomalies and triggering emergency strategies are as follows: The central control platform sends the best mission planning plan to the drone through the Internet of Things. The drone executes the mission according to the best mission planning plan and monitors the key flight parameters such as flight altitude, flight speed, remaining power, acceleration, ambient wind speed, and path deviation in real time. The flight status score F is obtained through the flight status monitoring function status , the expression is: Where, h represents the flight altitude, h ref represents the ideal flight altitude, v represents the flight speed, and v ref Indicates the ideal flight speed. represents acceleration, It represents the maximum acceleration value that the drone can withstand, k represents the power response coefficient, E min represents the minimum remaining power threshold, f represents the path deviation, and f max represents the maximum path deviation; F status The value range of is [0, +∞]; Set thresholds T1 and T2, when F status When ≤T1, it means the flight status is normal and the mission can continue; When T1 < F status When T2, it indicates a slight abnormality in the flight state and parameter adjustment is required; When T2≤F status , it indicates that the flight status is seriously abnormal and the emergency strategy is triggered.

7. The method for controlling a drone based on the Internet of Things according to claim 6, characterized in that: The triggering emergency strategy comprises the following specific steps: When T2≤F status When , the abnormal detection phase is entered, and the source of the problem is located through the fault location function. The expression is: Among them, F cause represents the abnormal cause location function, k ′ represents the wind speed response coefficient, w represents the ambient wind speed, and w crit Indicates the critical value of ambient wind speed; Set the threshold T3, when T3≤F cause When , it means the fault is minor, and the task path can be replanned to continue the task; When T3>F cause If the error message appears, it indicates a serious fault and the aircraft will immediately land in the nearest safe area, stop the mission and upload the abnormal status to the central control platform.

8. An Internet of Things-based drone control system, based on the Internet of Things-based drone control method according to any one of claims 1 to 7, characterized in that: include, Central control platform module: As the command center of the system, it is responsible for receiving mission requirements, generating preliminary mission planning solutions, and coordinating the work of various subsystems; Communication network module: Use the Internet of Things technology to achieve data transmission between the drone and the central control platform to ensure the real-time and accuracy of information; Environmental perception module: collects data about the surrounding environment through various sensors installed on the drone to provide raw data for subsequent processing; Data preprocessing module: performs preliminary processing such as cleaning and formatting on the data collected from the environment to improve data quality and availability; Intelligent decision support module: Combines pre-processed environmental data with current task requirements and uses algorithm models to dynamically adjust task planning plans to optimize execution efficiency; Flight control module: guides the UAV to fly automatically according to the final mission planning plan, and also has obstacle avoidance function; Status monitoring module: Continuously monitors the operating status and health of the drone, and can respond promptly and take corresponding measures when abnormalities are found; Emergency response module: When encountering an emergency or receiving an abnormal signal, the emergency plan is quickly activated to ensure the safe landing of the drone or other necessary safety operations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the drone control method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling a drone based on the Internet of Things according to any one of claims 1 to 7 are implemented.

Images