A method for locating emergency supplies for medical emergency drones

Abstract

The present invention discloses a method for locating emergency supplies for a medical emergency drone, relating to the field of drone control technology. The method comprises the following steps: retrieving the drone's flight status information, performing aerodynamic interference prediction analysis, and obtaining a normal flight status and an abnormal flight status; performing discriminant processing on the flight status feature vector of the abnormal flight status to generate a flight anomaly identification signal or a normal flight signal; when the flight anomaly identification signal is generated, performing aerodynamic field modeling analysis to obtain an aerodynamic interference prediction map or a low-interference area distribution signal; and when the normal flight signal is generated, obtaining a coordination control signal, an active interference suppression signal, or a passive emergency protection signal. The present invention prevents the occurrence of problems such as emergency protection failure and positioning loss through active aerodynamic interference suppression, multi-dimensional decision-making evaluation, adaptive control, and independent supply positioning, thereby improving the reliability and supply delivery capability of drones in extreme weather conditions.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a method for positioning emergency supplies of a medical emergency UAV. Background Art

[0002] In medical emergency and disaster relief scenarios, medical emergency drones must perform supply delivery missions under extreme weather conditions. However, when drones deploy parachutes to deliver supplies in adverse weather conditions such as typhoons and strong gusts, the downwash generated by the high-speed rotating rotors and the parachute deployment process can cause severe aerodynamic interference. Combined with lateral wind shear, this creates a complex turbulent field, which can prevent the parachute from deploying properly or cause it to deflect significantly after deployment.

[0003] In existing technologies, a drone's wind-resistant attitude stabilization system and emergency landing system are designed independently and lack a coordinated mechanism. Traditional emergency protection systems utilize a passive trigger mechanism, activating the parachute system only upon detecting an abnormal descent. During this time, the rotors continue to operate at high speeds, creating a strong downwash that directly impacts the deployed parachute, causing deformation, tearing, or entanglement, severely impacting the effectiveness of emergency protection. Furthermore, existing methods for locating medical supplies primarily rely on the drone's own positioning system. If the drone experiences attitude loss or system failure during an emergency landing, this can easily lead to loss of supply location information, impacting subsequent recovery and the completion of emergency rescue missions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for locating emergency supplies for a medical emergency drone, which solves the problems existing in the background technology.

[0005] To solve the above technical problems, the present invention provides a method for locating emergency supplies using a medical emergency drone, comprising the following steps:

[0006] Step 1: Retrieve the flight status information of the UAV and perform aerodynamic interference prediction analysis on the flight status information to obtain normal flight status and abnormal flight status;

[0007] Step 2: performing discriminative processing on the flight state feature vector of the abnormal flight state to generate a flight abnormality identification signal or a normal flight signal;

[0008] Step 3: When the flight anomaly identification signal is generated, the collected UAV rotor operating parameter information is subjected to aerodynamic field modeling and analysis to obtain an aerodynamic interference prediction map or a low-interference area distribution signal;

[0009] Step 4: When generating a normal flight signal, perform multi-source data fusion processing on the collected UAV system status information, and process the obtained coordinated control instructions and emergency program trigger instructions to obtain a coordinated control signal; perform rotor deceleration strategy matching analysis on the collected aerodynamic interference prediction map, and compare and analyze the obtained rotor control optimization parameters to obtain an active interference suppression signal or a passive emergency protection signal;

[0010] The process of performing rotor deceleration strategy matching analysis in step 4 includes:

[0011] S4b1. Obtaining an aerodynamic interference prediction map of the UAV within a monitoring time threshold, wherein the aerodynamic interference prediction map represents a distribution value of an interference coefficient;

[0012] S4b2, integrating the interference coefficient with the system safety factor to calculate the final rotor control optimization parameters;

[0013] S4b3. Compare and analyze the rotor control optimization parameter with a preset rotor control optimization parameter threshold to obtain an active interference suppression signal or a passive emergency protection signal.

[0014] The analysis process of the interference coefficient distribution value in the aerodynamic interference prediction map includes:

[0015] Obtain the estimated rotor downwash speed of the UAV within the monitoring time threshold, as well as the maximum allowable wind speed for the safe deployment of the parachute configured on the UAV;

[0016] The estimated rotor downwash speed of the UAV within the monitoring time threshold is compared with the maximum allowable wind speed for safe deployment of a parachute configured for the UAV to obtain an interference coefficient.

[0017] Preferably, the process of collecting flight status information in step 1 includes:

[0018] S11, collecting the operating time period of the drone and setting the operating time period as the monitoring time threshold;

[0019] S12, setting each sensor configured on the drone as a state monitoring node, and obtaining flight state information of each state monitoring node within a monitoring time threshold, wherein the flight state information represents altitude change rate and attitude angular velocity data;

[0020] S13. The flight status information of the status monitoring node is judged and processed. If abnormal status information of the status monitoring node is generated, a flight abnormality identification signal is generated, and the status monitoring node corresponding to the flight abnormality identification signal is set to an abnormal flight status. If no abnormal status information is generated, a normal flight signal is generated, and the status monitoring node corresponding to the normal flight signal is set to a normal flight status.

[0021] Preferably, the step of determining and processing the flight status information in step 2 includes:

[0022] S21. Obtaining a preset abnormality determination duration for an abnormal flight state within a monitoring time threshold;

[0023] S22. Obtain the duration of the abnormal flight state within the monitoring time threshold from the time when the most recent normal flight ended to the current time, and set it as the abnormal duration;

[0024] S23. Setting the value obtained by subtracting the abnormality duration from the preset abnormality determination duration as the flight state feature vector;

[0025] S24. Perform discrimination processing on the flight status feature vector to obtain a flight abnormality identification signal or a normal flight signal.

[0026] Preferably, the process of performing aerodynamic field modeling analysis in step three includes:

[0027] S31. Obtaining operating parameter information of the UAV rotor within a monitoring time threshold, including rotor speed and pitch angle data;

[0028] S32, extracting the numerical values ​​of the rotor speed, and setting a data set consisting of the extracted numerical values ​​as a speed parameter set;

[0029] S33, obtaining the current optimal operating parameters of the UAV rotor, and setting a data set consisting of the numerical values ​​of the current optimal operating parameters as the optimal parameter set;

[0030] S34. Compare and analyze the speed parameter set with the optimal parameter set to obtain an interference intensity instruction or a normal working signal.

[0031] Preferably, when generating the interference intensity instruction, the following steps are also included:

[0032] Compare and analyze the pitch angle data with the preset pitch angle threshold to obtain an aerodynamic interference prediction map or a low-interference area distribution signal;

[0033] The pitch angle data represents the product of the current pitch angle setting value of the UAV rotor and the pitch angle change rate after data normalization processing, and the pitch angle change rate represents the rate of change between the moment the angle adjustment instruction is generated and the moment the angle adjustment is completed.

[0034] Preferably, the process of performing multi-source data fusion processing in step 4 includes:

[0035] S4a1. Obtaining the system status information of the UAV within the monitoring time threshold, including flight altitude data and ambient wind speed data;

[0036] S4a2, comparing and analyzing the flight altitude data and the ambient wind speed data with a preset flight altitude threshold and a preset ambient wind speed threshold;

[0037] S4a3. Count the number of flight altitude risk indexes and environmental risk indexes that are greater than or equal to their respective preset thresholds, record them as the total number of risk items, and calculate the system safety factor based on the total number of risk items.

[0038] Preferably, the flight altitude data represents the product of the difference between the current flight altitude of the UAV and the minimum safe flight altitude after data normalization and the altitude change rate, wherein the altitude change rate represents the altitude change of the UAV per unit time;

[0039] The environmental wind speed data represents the product of the value corresponding to the real-time wind speed value of the drone's environment exceeding the preset wind speed safety threshold and the wind direction stability coefficient after data normalization. The wind direction stability coefficient represents the standard deviation of the drone's wind direction change within a preset time window.

[0040] Beneficial effects

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. Build a coupled control mechanism for rotor control and parachute release. By predicting and actively suppressing aerodynamic interference, adjust the rotor state in advance to create a low-interference environment, achieving a transition from passive emergency response to active intervention, thereby improving the reliability of emergency protection.

[0043] 2. Adopt multi-dimensional state assessment and redundant design, and through quantitative indicators such as flight state characteristic vectors and system safety factors, avoid misjudgment problems caused by single threshold judgments, enhance decision-making robustness, and ensure that the system can still guarantee basic functions in the event of partial failure.

[0044] 3. By designing an adaptive intelligent control strategy, the rotor deceleration strategy is dynamically adjusted according to the interference coefficient and the system safety factor to achieve precise matching of emergency response, optimize resource utilization efficiency, and avoid system risks caused by excessive intervention.

[0045] 4. By configuring an independent material separation and positioning unit, it can work independently after the materials are separated from the drone, ensuring that even if the drone loses control and crashes, the location of the materials can still be tracked. There is no need to rely entirely on the positioning of the drone itself, ensuring the recyclability of emergency materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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.

[0047] Figure 1 This is a flow chart of a method for locating emergency supplies using a medical emergency drone according to the present invention. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] In the description of the present invention, the logical order indicated by terms such as "step one" and "step two" is intended to facilitate the description of the present invention and simplify the operation, and does not mean that the devices or elements referred to must have a specific physical order, and therefore cannot be understood as a limitation on the present invention.

[0050] Example 1:

[0051] See also Figure 1 The present invention provides a method for locating emergency supplies of a medical emergency drone, comprising the following steps:

[0052] Step 1: Retrieve the flight status information of the UAV and perform aerodynamic interference prediction analysis on the flight status information to obtain normal flight status and abnormal flight status;

[0053] Step 2: performing discriminative processing on the flight state feature vector of the abnormal flight state to generate a flight abnormality identification signal or a normal flight signal;

[0054] Step 3: When the flight anomaly identification signal is generated, the collected UAV rotor operating parameter information is subjected to aerodynamic field modeling and analysis to obtain an aerodynamic interference prediction map or a low-interference area distribution signal;

[0055] Step 4: When generating a normal flight signal, perform multi-source data fusion processing on the collected UAV system status information, and process the obtained coordinated control instructions and emergency program trigger instructions to obtain a coordinated control signal; perform rotor deceleration strategy matching analysis on the collected aerodynamic interference prediction map, and compare and analyze the obtained rotor control optimization parameters to obtain an active interference suppression signal or a passive emergency protection signal;

[0056] In a preferred embodiment, the emergency supply positioning method for a medical emergency drone establishes a closed-loop, proactive emergency protection and supply positioning process. The core purpose of this method is to address the severe aerodynamic interference between the downwash generated by the drone's high-speed rotating rotors and the emergency parachute deployment process in extreme weather conditions.

[0057] The overall working logic of this method is that, in its initial stage, it focuses on achieving real-time, high-precision monitoring and qualitative identification of the UAV's flight attitude through steps one and two. This aims to accurately distinguish whether the UAV is in a controllable, normal flight state or has entered an abnormal flight state such as stall or severe turbulence. The execution path of the method branches based on the judgment result: if the judgment result is abnormal, step three is immediately initiated. Its core task is to quickly establish a dynamic aerodynamic interference field model based on the current rotor operating parameters. The aerodynamic interference prediction map output by this step provides a key decision-making basis for the subsequent active interference suppression strategy. Conversely, if the judgment result is normal, the system executes step four. It assesses the overall system safety level through multi-source data fusion and uses the interference map to formulate an optimal rotor deceleration strategy. The ultimate goal is to generate clear control signals to actively adjust the rotor state to create a low-interference safety window to ensure the smooth deployment of the parachute. Through the coordinated work of these four steps, this method realizes a complete closed loop from state perception, risk prediction, and active control, significantly improving the success rate of emergency material delivery and the reliability of material positioning under extreme conditions.

[0058] The process of performing rotor deceleration strategy matching analysis in step 4 includes:

[0059] S4b1. Obtaining an aerodynamic interference prediction map of the UAV within a monitoring time threshold, wherein the aerodynamic interference prediction map represents a distribution value of an interference coefficient;

[0060] S4b2, integrating the interference coefficient with the system safety factor to calculate the final rotor control optimization parameters;

[0061] S4b3. Compare and analyze the rotor control optimization parameter with a preset rotor control optimization parameter threshold to obtain an active interference suppression signal or a passive emergency protection signal.

[0062] The analysis process of the interference coefficient distribution value in the aerodynamic interference prediction map includes:

[0063] Obtain the estimated rotor downwash speed of the UAV within the monitoring time threshold, as well as the maximum allowable wind speed for the safe deployment of the parachute configured on the UAV;

[0064] The estimated rotor downwash speed of the UAV within the monitoring time threshold is compared with the maximum allowable wind speed for safe deployment of a parachute configured for the UAV to obtain an interference coefficient.

[0065] The interference coefficient calculation formula is:

[0066]

[0067] represents the interference coefficient; represents the estimated rotor downwash speed of the UAV within the monitoring time threshold; Indicates the maximum permissible wind speed for the drone's parachute to deploy safely;

[0068] The interference coefficient objectively reflects the risk level of downwash to parachute deployment. >1, indicating a significant risk;

[0069] The rotor deceleration strategy matching and analysis process of this embodiment constitutes the decision-making center for actively suppressing aerodynamic interference;

[0070] In S4b1, this process obtains an aerodynamic interference prediction map. To clarify the physical significance of this map, the core data in the map, the interference coefficient, is a dimensionless indicator used to quantify the risk level posed by rotor downwash to parachute deployment. Its calculation logic is to compare the estimated speed of the rotor downwash with the maximum allowable wind speed for safe parachute deployment, and obtain a ratio. When this ratio is greater than 1, it indicates that the downwash speed has exceeded the parachute's safety limit, and there is a significant deployment risk.

[0071] In S4b2, the system performs a key matching analysis and converts the interference coefficient and system safety factor Fusion is performed to calculate the final rotor control optimization parameters To ensure that the risk level can be magnified when the system safety factor is low, an optimized calculation method is to use division, the formula is as follows:

[0072]

[0073] represents the final rotor control optimization parameters; represents the interference coefficient; Indicates the system safety factor;

[0074] This formula ensures that the system safety factor When lowering, the rotor control optimization parameters The parameter will increase significantly, thereby more sensitively triggering the subsequent active interference suppression signal, which is consistent with the active safety design concept of the invention. The design logic of this parameter is intended to reflect a comprehensive risk level, and its value is determined by the interference coefficient and the system safety factor. Specifically, the value of this parameter is proportional to the interference coefficient and inversely proportional to the system safety factor. This structure ensures that when the direct interference threat is high or when the system's own safety status is poor, the optimization parameter will increase significantly, indicating a higher overall risk.

[0075] In S4b3, the system compares the calculated rotor control optimization parameters with a preset control threshold; the setting of this threshold is determined based on a large amount of flight test data and safety redundancy considerations; if the parameter exceeds the threshold, it indicates that the overall risk has exceeded the acceptable range and active intervention must be taken. The system will generate an active interference suppression signal and instruct the rotor control system to perform specific deceleration or pitch adjustment actions; if the parameter does not exceed the threshold, it indicates that the risk is controllable, and the system will generate a passive emergency protection signal to maintain standard emergency procedures on standby; through this series of steps, this method transforms complex flight control problems into a clear parameter matching and threshold judgment process, realizing active, precise and intelligent suppression of rotor interference.

[0076] Example 2:

[0077] The process of collecting flight status information in step 1 includes:

[0078] S11, collecting the operating time period of the drone and setting the operating time period as the monitoring time threshold;

[0079] S12, setting each sensor configured on the drone as a state monitoring node, and obtaining flight state information of each state monitoring node within a monitoring time threshold, wherein the flight state information represents altitude change rate and attitude angular velocity data;

[0080] S13. Determine and process the flight status information of the status monitoring node. If abnormal status information of the status monitoring node is generated, generate a flight abnormality identification signal, and set the status monitoring node corresponding to the flight abnormality identification signal to an abnormal flight status. If no abnormal status information is generated, generate a normal flight signal, and set the status monitoring node corresponding to the normal flight signal to a normal flight status.

[0081] The step of determining and processing the flight status information in step 2 includes:

[0082] S21. Obtaining a preset abnormality determination duration for an abnormal flight state within a monitoring time threshold;

[0083] S22. Obtain the duration of the abnormal flight state within the monitoring time threshold from the time when the most recent normal flight ended to the current time, and set it as the abnormal duration;

[0084] S23. Setting the value obtained by subtracting the abnormality duration from the preset abnormality determination duration as the flight state feature vector;

[0085] S24, performing discrimination processing on the flight status feature vector to obtain a flight abnormality identification signal or a normal flight signal;

[0086] In a specific configuration of this embodiment, the process of sensing and determining the flight status of the drone is designed as a progressive, refined process from data collection to in-depth analysis;

[0087] To achieve continuous monitoring of the flight status, the system executes S11 and defines a time window, namely, a monitoring time threshold (for example, set to 100 milliseconds). In S12, the system logically defines multiple onboard sensors, such as air pressure sensors, accelerometers, and gyroscopes, as status monitoring nodes, which continuously collect and output flight status information processed into altitude change rate and attitude angular velocity data. To ensure the accuracy of the judgment, the system sets a normal range based on the known standards in the field of UAV flight control. For example, during stable flight, the altitude change rate should be less than 0.5 meters per second, and the attitude angular velocity should be less than 5 degrees per second.

[0088] In S13, the system makes a preliminary judgment on the collected flight status information; when any indicator exceeds the preset normal range, the system generates preliminary abnormal status information and produces a flight abnormality identification signal; in order to enhance the robustness of the decision and avoid overreaction to transient disturbances, the system further performs the step of judging and processing the flight status information and makes an in-depth judgment on the abnormal state; in S21 and S22, the system introduces the consideration of the time dimension by calculating the duration of the abnormality; in S23, the system constructs a flight status feature vector, whose value directly reflects the severity and persistence of the abnormal state; the calculation logic of the vector is: a preset, The abnormality judgment time representing the upper tolerance limit (for example, set to 1 second) is subtracted from the duration of the abnormality; a large positive value indicates that the abnormality has just occurred, while a negative value indicates that the abnormality has continued for more than the upper tolerance limit of the system; it should be understood that the specific value of the abnormality judgment time is not fixed, but can be dynamically adjusted according to the model, mission profile and environmental risk level of the UAV through statistical analysis of historical flight data, aiming to achieve the optimal balance between risk identification sensitivity and system stability; finally, in S24, the system makes a final judgment based on the final value of the feature vector, and outputs a flight abnormality identification signal or a normal flight signal that determines the direction of the subsequent process.

[0089] Example 3:

[0090] The process of performing aerodynamic field modeling analysis in step 3 includes:

[0091] S31. Obtaining operating parameter information of the UAV rotor within a monitoring time threshold, including rotor speed and pitch angle data;

[0092] S32, extracting the numerical values ​​of the rotor speed, and setting a data set consisting of the extracted numerical values ​​as a speed parameter set;

[0093] S33, obtaining the current optimal operating parameters of the UAV rotor, and setting a data set consisting of the numerical values ​​of the current optimal operating parameters as the optimal parameter set;

[0094] S34, comparing and analyzing the speed parameter set with the optimal parameter set to obtain an interference intensity instruction or a normal working signal;

[0095] When generating the interference intensity instruction, the following steps are also included:

[0096] Compare and analyze the pitch angle data with the preset pitch angle threshold to obtain an aerodynamic interference prediction map or a low-interference area distribution signal;

[0097] The pitch angle data represents the product of the current pitch angle setting value of the UAV rotor and the pitch angle change rate after data normalization processing, and the pitch angle change rate represents the rate of change between the time when the angle adjustment instruction is generated and the time when the angle adjustment is completed;

[0098] To achieve accurate prediction of rotor downwash interference, this method immediately initiates a dynamic aerodynamic field modeling and analysis process upon receiving a flight anomaly signal.

[0099] The process begins at S31, where the system obtains the operating parameter information of the UAV rotor within the current monitoring time threshold, with the core being the rotor speed and pitch angle data. In S32 and S33, the system constructs the real-time rotor speed values ​​into a speed parameter set and compares it with a preset optimal parameter set representing an ideal low-interference state. When the root mean square error (RMSE) of the speed parameter set relative to the optimal parameter set exceeds a preset threshold, the system generates an interference intensity instruction in S34, indicating a high risk of aerodynamic interference.

[0100] On this basis, in order to make the prediction model more refined, the system introduces an analysis of the pitch angle; the pitch angle data is a composite quantitative indicator used to characterize the dynamic aerodynamic characteristics of the rotor; its internal logic is that the value of this indicator is not only determined by the static pitch angle, but also comprehensively considers two core factors: one is the current pitch angle setting value, and the other is the rate of change of the pitch angle; its calculation method makes the final indicator value be amplified as the pitch angle change rate increases, so that it can not only reflect the current thrust state, but also predict the dynamic aerodynamic effect during the pitch adjustment process; when the system generates an interference intensity instruction, it will further compare this composite pitch angle data with a preset pitch angle threshold; the threshold defines the acceptable aerodynamic influence range; the comparison result is finally used to draw an aerodynamic interference prediction map, which is a data structure, such as a polar coordinate array, in which each element stores a calculated interference coefficient value, intuitively showing the interference intensity distribution in different directions around the drone.

[0101] Example 4:

[0102] The process of multi-source data fusion processing in step 4 includes:

[0103] S4a1. Obtaining the system status information of the UAV within the monitoring time threshold, including flight altitude data and ambient wind speed data;

[0104] S4a2, comparing and analyzing the flight altitude data and the ambient wind speed data with a preset flight altitude threshold and a preset ambient wind speed threshold;

[0105] S4a3. Counting the number of flight altitude risk indexes and environmental risk indexes that are greater than or equal to their respective preset thresholds, recording the number as the total number of risk items, and calculating the system safety factor based on the total number of risk items;

[0106] The flight altitude data represents the product of the difference between the current flight altitude of the UAV and the minimum safe flight altitude after data normalization and the altitude change rate, wherein the altitude change rate represents the altitude change of the UAV per unit time;

[0107] The environmental wind speed data represents the product of the value corresponding to the real-time wind speed value of the drone's environment exceeding the preset wind speed safety threshold and the wind direction stability coefficient after data normalization. The wind direction stability coefficient represents the standard deviation of the drone's wind direction change within a preset time window;

[0108] According to the total number of risk items Calculating system safety factors , and its calculation formula is:

[0109]

[0110] is the total number of risk items under assessment. When no risk item exceeds the standard, =0, =1, represents the safest; when one item exceeds the standard =1, =0.5; when both items exceed the standard =2, =0, represents the most dangerous; this definition is completely consistent with the intention of the present invention.

[0111] To ensure the comparability of risk metrics from different sources and facilitate subsequent integration, a more optimized implementation is to normalize all factors involved in the risk calculation to obtain a dimensionless risk index. Therefore, the revised calculation formula for "flight altitude data" as an "altitude risk index" is:

[0112]

[0113] is a dimensionless high risk index; represents the normalization function; is the current flight altitude; The minimum safe flight altitude; is the rate of change of altitude;

[0114] When the UAV is in a normal flight state, this method performs a multi-source data fusion processing process, the core purpose of which is to generate a quantitative indicator that can comprehensively evaluate the current flight risk - the system safety factor;

[0115] The process begins at S4a1, where the system acquires the drone's comprehensive system status information. To further clarify, this data is not simply raw readings. The flight altitude data is defined as a vertical risk metric. Its inherent logic is that the magnitude of the risk is proportional to the drone's vertical speed and inversely proportional to its margin from the minimum safe altitude. In other words, the faster the drone descends, or the closer it gets to the safety baseline, the higher the risk metric.

[0116] Similarly, the ambient wind speed data is defined as a risk metric for the external environment. The logic is that risk is determined by both the absolute magnitude of the wind speed and its instability. This metric increases as the current wind speed exceeds the safety threshold and is further amplified by drastic changes in wind direction (i.e., high wind direction standard deviation, indicating strong gusts or wind shear).

[0117] In S4a2 and S4a3, the system adopts a clear and robust way to combine these two risk metrics and calculate the final system safety factor; this method does not perform a complex weighted sum of the two risk indices, but directly determines whether they exceed their respective safety thresholds; as defined in the previous formula, the system counts the number of risk items that exceed the threshold To directly calculate the system safety factor The advantages of this method are its simplicity and robustness, and it avoids the difficulty of setting and adjusting complex weight parameters for different risk sources. The final system safety factor is between 0 and 1, and its physical meaning is clear: a value of 1 represents complete safety, and the lower the value, the more risk items are faced, and the less safe the system as a whole is, which is completely consistent with the design purpose of the present invention.

[0118] 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 method for locating emergency supplies by a medical emergency drone, characterized in that: The following steps are involved: Step 1: Retrieve the flight status information of the UAV and perform aerodynamic interference prediction analysis on the flight status information to obtain normal flight status and abnormal flight status; Step 2: performing discriminative processing on the flight state feature vector of the abnormal flight state to generate a flight abnormality identification signal or a normal flight signal; Step 3: When the flight anomaly identification signal is generated, the collected UAV rotor operating parameter information is subjected to aerodynamic field modeling and analysis to obtain an aerodynamic interference prediction map or a low-interference area distribution signal; Step 4: When generating a normal flight signal, perform multi-source data fusion processing on the collected UAV system status information, and process the obtained coordinated control instructions and emergency program trigger instructions to obtain a coordinated control signal; perform rotor deceleration strategy matching analysis on the collected aerodynamic interference prediction map, and compare and analyze the obtained rotor control optimization parameters to obtain an active interference suppression signal or a passive emergency protection signal; The process of performing rotor deceleration strategy matching analysis in step 4 includes: S4b1. Obtaining an aerodynamic interference prediction map of the UAV within a monitoring time threshold, wherein the aerodynamic interference prediction map represents a distribution value of an interference coefficient; S4b2, integrating the interference coefficient with the system safety factor to calculate the final rotor control optimization parameters; S4b3, comparing and analyzing the rotor control optimization parameter with a preset rotor control optimization parameter threshold to obtain an active interference suppression signal or a passive emergency protection signal; The analysis process of the interference coefficient distribution value in the aerodynamic interference prediction map includes: Obtain the estimated rotor downwash speed of the UAV within the monitoring time threshold, as well as the maximum allowable wind speed for the safe deployment of the parachute configured on the UAV; The estimated rotor downwash speed of the UAV within the monitoring time threshold is compared with the maximum allowable wind speed for safe deployment of a parachute configured for the UAV to obtain an interference coefficient.

2. The method for locating emergency supplies by a medical emergency drone according to claim 1, characterized in that: The process of collecting flight status information in step 1 includes: S11, collecting the operating time period of the drone and setting the operating time period as the monitoring time threshold; S12, setting each sensor configured on the drone as a state monitoring node, and obtaining flight state information of each state monitoring node within a monitoring time threshold, wherein the flight state information represents altitude change rate and attitude angular velocity data; S13. The flight status information of the status monitoring node is judged and processed. If abnormal status information of the status monitoring node is generated, a flight abnormality identification signal is generated, and the status monitoring node corresponding to the flight abnormality identification signal is set to an abnormal flight status. If no abnormal status information is generated, a normal flight signal is generated, and the status monitoring node corresponding to the normal flight signal is set to a normal flight status.

3. The method for locating emergency supplies by a medical emergency drone according to claim 2, characterized in that: The step of determining and processing the flight status information in step 2 includes: S21. Obtaining a preset abnormality determination duration for an abnormal flight state within a monitoring time threshold; S22. Obtain the duration of the abnormal flight state within the monitoring time threshold from the time when the most recent normal flight ended to the current time, and set it as the abnormal duration; S23. Setting the value obtained by subtracting the abnormality duration from the preset abnormality determination duration as the flight state feature vector; S24. Perform discrimination processing on the flight status feature vector to obtain a flight abnormality identification signal or a normal flight signal.

4. The method for locating emergency supplies by a medical emergency drone according to claim 1, characterized in that: The process of performing aerodynamic field modeling analysis in step 3 includes: S31. Obtaining operating parameter information of the UAV rotor within a monitoring time threshold, including rotor speed and pitch angle data; S32, extracting the numerical values ​​of the rotor speed, and setting a data set consisting of the extracted numerical values ​​as a speed parameter set; S33, obtaining the current optimal operating parameters of the UAV rotor, and setting a data set consisting of the numerical values ​​of the current optimal operating parameters as the optimal parameter set; S34. Compare and analyze the speed parameter set with the optimal parameter set to obtain an interference intensity instruction or a normal working signal.

5. The method for locating emergency supplies by a medical emergency drone according to claim 4, characterized in that: When generating the interference intensity instruction, the following steps are also included: Compare and analyze the pitch angle data with the preset pitch angle threshold to obtain an aerodynamic interference prediction map or a low-interference area distribution signal; The pitch angle data represents the product of the current pitch angle setting value of the UAV rotor and the pitch angle change rate after data normalization processing, and the pitch angle change rate represents the rate of change between the moment the angle adjustment instruction is generated and the moment the angle adjustment is completed.

6. The method for locating emergency supplies by a medical emergency drone according to claim 1, characterized in that: The process of multi-source data fusion processing in step 4 includes: S4a1. Obtaining the system status information of the UAV within the monitoring time threshold, including flight altitude data and ambient wind speed data; S4a2, comparing and analyzing the flight altitude data and the ambient wind speed data with a preset flight altitude threshold and a preset ambient wind speed threshold; S4a3. Count the number of flight altitude risk indexes and environmental risk indexes that are greater than or equal to their respective preset thresholds, record them as the total number of risk items, and calculate the system safety factor based on the total number of risk items.

7. The method for locating emergency supplies by a medical emergency drone according to claim 6, characterized in that: The flight altitude data represents the product of the difference between the current flight altitude of the UAV and the minimum safe flight altitude after data normalization and the altitude change rate, wherein the altitude change rate represents the altitude change of the UAV per unit time; The environmental wind speed data represents the product of the value corresponding to the real-time wind speed value of the drone's environment exceeding the preset wind speed safety threshold and the wind direction stability coefficient after data normalization. The wind direction stability coefficient represents the standard deviation of the drone's wind direction change within a preset time window.

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