Unmanned aerial vehicle flight path control system based on Internet of Things

Abstract

The invention discloses an unmanned aerial vehicle flight path control system based on the Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the system comprises an electromagnetic environment and flight state combined sensing module which is used for collecting channel data and flight state data; the channel-flight control coupling risk assessment module is used for calculating a navigable index based on the channel data and the flight state data; the navigable map construction and prediction module is used for constructing a navigable map based on the electromagnetic environment navigable index and predicting the navigable index; the predictive path adaptive planning module is used for generating a safe path based on the predicted navigable index; and the flight control instruction generation and execution module is used for converting the safe path into a flight control instruction and executing the flight control instruction. According to the method, the coupling instability risk is pre-judged through the electromagnetic environment navigable index, and the safety and task execution efficiency of the unmanned aerial vehicle in a complex electromagnetic environment are improved by adopting a dual-threshold grading coping strategy and combining with a multi-dimensional data fusion decision.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to an Internet of Things (IoT)-based UAV flight path control system. Background Art

[0002] In the field of drone control technology, flight control in complex electromagnetic environments is crucial. With the development of the Internet of Things (IoT), drones' communication and perception capabilities are constantly improving. However, the complex electromagnetic environment and the diversity of flight states present new challenges to drone flight path control.

[0003] In existing technologies, drone path control systems find it difficult to effectively assess the coupling risk between communication links and flight control, cannot accurately distinguish between benign disconnections and malicious interference in communication links, lack the ability to predict the risk of channel-flight control coupling instability, and often misattribute flight accidents, resulting in low safety and mission execution efficiency of drones in complex electromagnetic environments. Summary of the Invention

[0004] The purpose of the present invention is to provide a UAV flight path control system based on the Internet of Things, which solves the problems existing in the background technology.

[0005] To solve the above technical problems, the present invention provides a UAV flight path control system based on the Internet of Things, comprising: an electromagnetic environment and flight status joint perception module for collecting channel data of the UAV communication link and its own flight status data; a channel-flight control coupling risk assessment module, configured to calculate an electromagnetic environment flyability index representing flight risk based on the channel data and flight status data; A navigability map construction and prediction module is used to construct a navigability map representing the spatial risk distribution based on the historical and real-time electromagnetic environment navigability index, and predict the navigability index of future waypoints on the scheduled route; a predictive path adaptive planning module, configured to adaptively adjust the predetermined route based on the predicted navigability index to generate a safe path; The flight control instruction generation and execution module is used to convert the safety path into specific flight control instructions and execute them.

[0006] Preferably, the channel data includes channel state information, signal-to-noise ratio and bit error rate; the flight state data includes three-dimensional position, speed, linear acceleration and angular velocity.

[0007] Preferably, the predictive path adaptive planning module adaptively adjusts the scheduled route in the following manner: comparing the predicted airworthiness index with preset critical thresholds and warning thresholds; In response to the predicted navigability index being below the critical threshold, initiating path replanning based on the navigability map; In response to the predicted navigability index being between the critical threshold and the warning threshold, a risk degradation operation is performed by reducing a target speed instruction or decreasing an upper limit of a path curvature.

[0008] Preferably, the critical threshold is determined by calibrating the electromagnetic environment navigability index that causes control divergence in an experiment and taking the safety statistical lower bound of the electromagnetic environment navigability index data distribution; the warning threshold is a numerical value set based on the critical threshold at a preset multiple.

[0009] Preferably, the path replanning adopts a heuristic path search algorithm. When searching for the optimal path in the navigability map, the algorithm makes a decision based on the actual travel cost from the starting point to the current point and the estimated travel cost from the current point to the end point. The travel cost of each point is inversely proportional to the electromagnetic environment navigability index.

[0010] Preferably, the channel-flight control coupling risk assessment module calculates the electromagnetic environment navigability index in the following manner: Based on the channel data and the flight status data, respectively calculating a comprehensive communication link quality index, a communication link volatility index, and a flight control instruction sensitivity index; Multiplying the communication link volatility index by the flight control instruction sensitivity index, multiplying the product by a preset channel-flight control coupling coefficient, and summing the result with a value of 1 to obtain a risk denominator; The electromagnetic environment navigability index is obtained by taking the comprehensive communication link quality index as the numerator and dividing it by the risk denominator.

[0011] Preferably, the comprehensive communication link quality index is calculated by normalizing the signal-to-noise ratio, channel state information and bit error rate in the channel data, and then performing weighted summation on the processed values ​​based on their respective preset weight coefficients.

[0012] Preferably, the communication link volatility index is calculated by calculating the sample standard deviation of the comprehensive communication link quality index time series within a preset time window.

[0013] Preferably, the flight control instruction sensitivity index is calculated by respectively calculating the ratio of the current linear acceleration and angular velocity to the preset maximum values ​​of the current linear acceleration and angular velocity, and then weightedly summing the two ratios based on their respective preset weights, and ensuring that the result is not greater than 1 through a limiting function.

[0014] The present invention has the following beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: 1. By calculating the electromagnetic environment navigability index, the risk of channel-flight control coupling instability can be predicted, potential dangers can be avoided in advance, and the transition from passive response to active prediction can be achieved.

[0015] 2. By adopting dual-threshold decision-making logic, an intelligent graded risk response strategy is provided, and corresponding different measures are taken at different risk stages, which can ensure both safety and task efficiency.

[0016] 3. Through multi-dimensional data fusion decision-making, comprehensive collection of deep channel data and complete flight status data improves the accuracy and reliability of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can derive other drawings based on these drawings without inventive effort. Figure 1 It is a logic block diagram of the system of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the scope of protection of the present invention. Example 1: See also Figure 1 , the present invention provides a UAV flight path control system based on the Internet of Things, comprising: an electromagnetic environment and flight status joint perception module for collecting channel data of the UAV communication link and its own flight status data; a channel-flight control coupling risk assessment module, configured to calculate an electromagnetic environment flyability index representing flight risk based on the channel data and flight status data; A navigability map construction and prediction module is used to construct a navigability map representing the spatial risk distribution based on the historical and real-time electromagnetic environment navigability index, and predict the navigability index of future waypoints on the scheduled route; a predictive path adaptive planning module, configured to adaptively adjust the predetermined route based on the predicted navigability index to generate a safe path; A flight control instruction generation and execution module, configured to convert the safety path into specific flight control instructions and execute them; This embodiment provides an IoT-based UAV flight path control system. Its fundamental innovation lies in the construction of a complete closed loop from data perception, risk quantification, situation prediction, to decision-making and execution. Through the collaborative work of a joint electromagnetic environment and flight status perception module, a channel-flight control coupling risk assessment module, a navigability map construction and prediction module, a predictive path adaptive planning module, and a flight control command generation and execution module, the system deeply couples and integrates the traditionally separate issues of communication link quality and flight control stability. The direct result of this design concept is that the system no longer passively responds to communication interruptions, but can actively predict and avoid the risk of channel-flight control coupling instability caused by the coupling of communication link fluctuations and violent flight maneuvers. This paradigm shift from passive response to active prediction eliminates a large number of potential flight accidents at the root, thereby greatly improving the operational safety and mission success rate of UAVs in complex electromagnetic environments.

[0019] Example 2: The channel data includes channel state information, signal-to-noise ratio and bit error rate; the flight state data includes three-dimensional position, velocity, linear acceleration and angular velocity; In this embodiment, the electromagnetic environment and flight status joint perception module collects data with unprecedented depth and breadth to accurately quantify flight risks. Specifically, the channel data is not limited to a single signal strength indicator, but includes channel state information (CSI), signal-to-noise ratio (SNR), and bit error rate (BER), which can deeply characterize the physical layer characteristics of the link. This set of data together constitutes a comprehensive portrayal of the health of the communication link. At the same time, the flight status data includes the three-dimensional position, velocity, linear acceleration, and angular velocity required for a complete kinematic description of the drone. The ingenuity of this data collection strategy lies in that it provides the subsequent channel-flight control coupling risk assessment module with sufficiently rich and highly correlated channel data and flight status data, ensuring the accuracy and reliability of risk assessment. This constitutes a solid data foundation for achieving the transition from passive response to active prediction and avoidance technology.

[0020] Example 3: The predictive path adaptive planning module adaptively adjusts the scheduled route in the following ways: comparing the predicted airworthiness index with preset critical thresholds and warning thresholds; In response to the predicted navigability index being below the critical threshold, initiating path replanning based on the navigability map; In response to the predicted navigability index being between the critical threshold and the warning threshold, performing a risk degradation operation by reducing a target speed command or lowering an upper limit of a path curvature; The critical threshold is determined by calibrating the electromagnetic environment navigability index that causes control divergence in an experiment and taking the safety statistical lower bound of the electromagnetic environment navigability index data distribution; the warning threshold is a value set based on the critical threshold at a preset multiple; The predictive path adaptive planning module of this embodiment is the decision-making core of the entire system. Its operating logic demonstrates a high degree of intelligence and security. Its core is a dual-threshold decision-making mechanism, including key thresholds. With warning threshold The threshold setting logic is a key step to ensure that those skilled in the art can reproduce and implement it. Critical thresholds The calibration process is directly related to the absolute safety of flight. Its setting method is a repeatable experimental process: in a simulation or actual test environment, the UAV is systematically injected with different intensities of channel interference and instructed to perform maneuvers of different intensities; the electromagnetic environment airworthiness index is fully recorded every time the flight control divergence occurs, such as when the attitude angle error exceeds the preset safety range. Finally, all the acquired instability The values ​​form a data set, That is, it is set as a safe statistical lower bound of the empirical cumulative distribution function of the data set, for example, it is set as the 5% quantile of the distribution; this method ensures that any value below The predicted values ​​will be regarded by the system as a high-risk state with a high probability of instability; Warning threshold is higher than , used to define a potential risk area; its value is set based on historical experimental data, which shows that the drone has non-instability but significant performance degradation, such as slight control jitter or significant increase in data transmission delay. A clear way to set the value range is to Set to A value between 1.5 and 2 times of the system’s maximum safety threshold; this design provides a valuable buffer zone and intervention window before the system enters a truly dangerous state; Based on this clear threshold logic, when the predictive path planning module detects the predicted navigability index of future path points on the scheduled route, Lower than When the system must immediately trigger path replanning, which is the highest level of safety response; Between and When the vehicle encounters a certain obstacle, the system prioritizes risk reduction operations, such as reducing the target speed command by a preset percentage or imposing a stricter constraint in the path planner to force a reduction in the upper limit of the path curvature. This graded response mechanism achieves a delicate balance between ensuring absolute safety and maintaining mission efficiency, demonstrating excellent engineering practicality.

[0021] Example 4: Path replanning uses a heuristic path search algorithm. When searching for the optimal path in the navigability map, the algorithm combines the actual cost from the starting point to the current point and the estimated cost from the current point to the end point to make a decision. The cost of each point is inversely proportional to the electromagnetic environment navigability index. In this embodiment, when path replanning is triggered, the core heuristic path search algorithm, such as the A* algorithm, is used. Its decision-making basis is its unique cost function. A key innovation is that the cost of each grid or node in the navigability map is , is set to the electromagnetic environment navigability index of the point An inversely proportional function; a clear and implementable cost setting is ,because is a normalized exponent, and the cost value will be between 0 and 1. The inherent logic of this design is that it cleverly transforms a complex physical concept, namely the airworthiness of the electromagnetic environment, into an intuitive mathematical quantity, namely the travel cost, which can be directly optimized by the path search algorithm. Therefore, the algorithm will instinctively avoid those The area with low value and high travel cost, that is, the area with bad electromagnetic environment, tends to pass through The final effect is that the algorithm not only finds the shortest path in terms of geometric distance, but also finds the optimal flight path that is the safest and most reliable in terms of electromagnetic environment, which is the core technical effect pursued by the present invention.

[0022] Example 5: The channel-flight control coupling risk assessment module calculates the electromagnetic environment airworthiness index in the following manner: Based on the channel data and the flight status data, respectively calculating a comprehensive communication link quality index, a communication link volatility index, and a flight control instruction sensitivity index; Multiplying the communication link volatility index by the flight control instruction sensitivity index, multiplying the product by a preset channel-flight control coupling coefficient, and summing the result with a value of 1 to obtain a risk denominator; Taking the comprehensive communication link quality index as a numerator and dividing it by the risk denominator, the electromagnetic environment navigability index is obtained; The comprehensive communication link quality index is calculated by normalizing the signal-to-noise ratio, channel state information, and bit error rate in the channel data, and then performing a weighted summation of the processed values ​​based on respective preset weight coefficients; The communication link volatility index is calculated by: calculating the sample standard deviation of the time series of the comprehensive communication link quality index within a preset time window; The flight control command sensitivity index is calculated by calculating the ratio of the current linear acceleration and angular velocity to the preset maximum values ​​of the current linear acceleration and angular velocity, then weighting the sum of the two ratios based on their respective preset weights, and using a clipping function to ensure that the result is no greater than 1; The core innovation of this embodiment is the channel-flight control coupling risk assessment module, which realizes the quantitative assessment of flight risks through a series of precise mathematical models; the module ultimately outputs the electromagnetic environment airworthiness index. , and its calculation process reflects a deep insight into the sources of risk; Calculating the electromagnetic environment navigability index The formula is: ; It represents the electromagnetic environment navigability index at spatial position p and time t. It is a dimensionless evaluation value and is the final output of this module. Indicates the overall communication link quality, also a dimensionless value; It represents the volatility of the communication link and is a dimensionless value; Indicates the sensitivity of flight control instructions, which is a dimensionless value; Represents the channel-flight control coupling coefficient, which is an adjustable dimensionless positive constant weight; This formula is the core original of this invention. Its design concept draws on the concept of safe flight envelope in the aviation field and applies it to electromagnetic space in a pioneering way. Its fundamental motivation is to deeply understand that the real threat to the UAV flight control system is not the poor communication quality alone, but the coupling effect of the violent fluctuation of the communication link and the sensitivity of the UAV's own control. The denominator It is precisely the precise mathematical modeling of this coupling risk, where the constant 1 is introduced to ensure that in the absence of risk, that is, or When it is 0, the denominator is not zero, which is a standard robustness design consideration; This formula calculates The value is the key criterion for accurately distinguishing between benign disconnection and malicious interference in the communication link. Its output value will directly serve as the core input of the navigability map construction and prediction module and the predictive path adaptive planning module, driving subsequent situation prediction and path decision-making, forming the cornerstone of the entire system's intelligent behavior. Coupling coefficient The calibration of the model is crucial to its performance; its determination method is a set of standard experimental calibration procedures: a large number of flight experiments in a controlled electromagnetic environment are carried out to systematically record the and Under these conditions, the critical data points that cause the UAV attitude to become unstable or the control delay to exceed the preset threshold are found; using these experimental data, the optimal fitting method is obtained through regression analysis or machine learning. value, so that A stable and highly sensitive risk warning can be given near the critical instability point; Calculate the overall communication link quality The formula is: ; They represent the dimensionless weight coefficients of SNR, CSI and BER respectively, all of which are positive constants and their sum is 1; ^ indicates normalization, a well-known data preprocessing technique that aims to uniformly map raw physical quantities of different dimensions and ranges to the range of 0 to 1 for weighted summation, such as normalized signal-to-noise ratio The formula Calculated, where and These are reasonable upper and lower limits preset based on the characteristics of the communication system; Represents the original physical quantities, which are directly output by the communication receiver physical layer in the electromagnetic environment and flight status joint perception module. They are directly obtainable measurement values. They represent the signal-to-noise ratio, the error vector magnitude of the channel state information, and the bit error rate. This formula is derived from the concept of multi-metric integrated link quality assessment in modern communications engineering. Practice has shown that a single metric cannot fully reflect the true health of a link. Therefore, this model integrates three key physical layer metrics: signal-to-noise ratio, channel state information, and bit error rate, to achieve a more comprehensive and accurate assessment result. Again, calculate the communication link volatility The formula is: ; Indicates the time window size used to calculate the standard deviation, that is, the number of samples, which is an adjustable positive integer parameter; represents the calculation result of Formula 2 at the past time point i; ¯ represents the arithmetic mean, That is, in the arrive Within the time window, all The arithmetic mean of the values; i represents the time step index in the summation operation, which is a standard mathematical notation; N means that the average value is calculated based on a sample window of size N; This formula is directly derived from the classic method of measuring the degree of data dispersion in statistics, namely the sample standard deviation; its core idea is borrowed here to quantify the quality of the communication link Stability over a period of time; a link with high volatility, even if its average quality is acceptable, may still hide significant instantaneous risks; Finally, calculate the flight control command sensitivity The formula is: ; They represent the dimensionless weights of linear acceleration and angular velocity, respectively, both of which are positive constants and their sum is 1; Represent the current linear acceleration vector and angular velocity vector of the UAV respectively. These two vectors are the original inputs and are measured and provided in real time by the inertial measurement unit (IMU) in the electromagnetic environment and flight status joint perception module; The Euclidean norm of a vector is a well-known method for calculating the magnitude of a vector. The maximum performance parameters of the drone are derived from the product technical specifications or performance manuals provided by the drone manufacturer. These values ​​are inherent properties of the device and are publicly available information available to those skilled in the art. is a clipping function, ensuring The final value of will not exceed 1, so that its physical meaning conforms to a normalized sensitivity index ranging from 0 to 1; This formula originates from robotics and vehicle dynamics. The linear acceleration and angular velocity of an aircraft are the most direct and recognized physical indicators for measuring the intensity of its motion state. The more intense the maneuvers performed by a drone, the higher the requirements for the real-time and accuracy of control commands, and the more sensitive it is to the stability of the communication link.

[0023] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A UAV flight path control system based on the Internet of Things, characterized in that: include: The electromagnetic environment and flight status joint perception module is used to collect the channel data of the UAV communication link and its own flight status data; a channel-flight control coupling risk assessment module, configured to calculate an electromagnetic environment flyability index representing flight risk based on the channel data and flight status data; A navigability map construction and prediction module is used to construct a navigability map representing the spatial risk distribution based on the historical and real-time electromagnetic environment navigability index, and predict the navigability index of future waypoints on the scheduled route; a predictive path adaptive planning module, configured to adaptively adjust the predetermined route based on the predicted navigability index to generate a safe path; A flight control instruction generation and execution module, configured to convert the safety path into specific flight control instructions and execute them; The channel-flight control coupling risk assessment module calculates the electromagnetic environment flyability index in the following manner: Based on the channel data and the flight status data, respectively calculating a comprehensive communication link quality index, a communication link volatility index, and a flight control instruction sensitivity index; Multiplying the communication link volatility index by the flight control instruction sensitivity index, multiplying the product by a preset channel-flight control coupling coefficient, and summing the result with a value of 1 to obtain a risk denominator; The electromagnetic environment navigability index is obtained by taking the comprehensive communication link quality index as the numerator and dividing it by the risk denominator.

2. The UAV flight path control system based on the Internet of Things according to claim 1, characterized in that: The channel data includes channel state information, signal-to-noise ratio and bit error rate; the flight state data includes three-dimensional position, speed, linear acceleration and angular velocity.

3. The UAV flight path control system based on the Internet of Things according to claim 1, characterized in that: The predictive path adaptive planning module adaptively adjusts the scheduled route in the following manner: comparing the predicted airworthiness index with preset critical thresholds and warning thresholds; In response to the predicted navigability index being below the critical threshold, initiating path replanning based on the navigability map; In response to the predicted navigability index being between the critical threshold and the warning threshold, a risk degradation operation is performed by reducing a target speed instruction or decreasing an upper limit of a path curvature.

4. The UAV flight path control system based on the Internet of Things according to claim 3, characterized in that: The critical threshold is determined by calibrating the electromagnetic environment navigability index that causes control divergence in an experiment and taking the safety statistical lower bound of the electromagnetic environment navigability index data distribution; the warning threshold is a value set based on the critical threshold at a preset multiple.

5. The UAV flight path control system based on the Internet of Things according to claim 3, characterized in that: The path replanning adopts a heuristic path search algorithm. When searching for the optimal path in the navigability map, the algorithm combines the actual travel cost from the starting point to the current point and the estimated travel cost from the current point to the end point to make a decision. The travel cost of each point is inversely proportional to the electromagnetic environment navigability index.

6. The UAV flight path control system based on the Internet of Things according to claim 1, characterized in that: The comprehensive communication link quality index is calculated by normalizing the signal-to-noise ratio, channel state information, and bit error rate in the channel data, and then performing weighted summation on the processed values ​​based on their respective preset weight coefficients.

7. The UAV flight path control system based on the Internet of Things according to claim 1, characterized in that: The communication link volatility index is calculated by calculating the sample standard deviation of the comprehensive communication link quality index time series within a preset time window.

8. The UAV flight path control system based on the Internet of Things according to claim 1, characterized in that: The flight control command sensitivity index is calculated as follows: the ratio of the current linear acceleration and angular velocity to the preset maximum values ​​of the current linear acceleration and angular velocity are calculated respectively, and then the two ratios are weighted summed based on their respective preset weights, and a limiting function is used to ensure that the result is not greater than 1.

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