A method, system and device for secure transmission and storage of multi-physical quantity data of a UAV, and a storage medium

Abstract

The application discloses a kind of unmanned plane multi-physical quantity data's safe transmission and storage method, system, equipment and storage medium, relate to unmanned plane communication technical field, method includes: communication link and flight state parameter are collected, link risk index and flight risk index are obtained respectively by filtering, checking and weighting processing;Combination environmental threat index is weighted and fused into comprehensive risk index;Risk change rate is calculated;According to comprehensive risk index and change rate, six progressive backup urgency is divided, and corresponding data protection operation is executed;The application realizes the dynamic quantification and trend prediction of the overall risk of unmanned plane, avoids the resource waste or key data loss of single static backup, significantly improves the survivability of data and flight safety in complex high-risk environment.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method, system, device, and storage medium for secure transmission and storage of multi-physical data from UAVs. Background Technology

[0002] With the widespread application of drones in logistics, agricultural plant protection, power line inspection, and security monitoring, drones need to transmit and store large amounts of multi-physical data in real time during missions. Currently, drone data backup often relies on passive methods such as scheduled uploads or resuming interrupted transmissions after link recovery. In the event of unforeseen risks, it is often impossible to save the last critical data. By establishing a progressive risk index calculation, it is possible to dynamically quantify the overall risk of drones, predict trends, and implement adaptive data protection, thereby improving the data survivability and flight safety of drones in complex and high-risk environments.

[0003] In existing technologies, UAV systems typically handle multiple physical data such as communication link quality, flight health status, and environmental threats in isolation in terms of data transmission, security, and storage, lacking the ability to perceive risks through multi-dimensional fusion. At the same time, data backup strategies are singular and static, failing to consider the evolution of risks, and storage media management is rudimentary, lacking hierarchical protection and proactive delivery mechanisms in extreme scenarios. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, device and storage medium for secure transmission and storage of multi-physical data of unmanned aerial vehicles.

[0005] Therefore, the technical problem solved by this invention is that in the existing UAV data transmission and storage process, there is a lack of multi-dimensional risk fusion assessment of communication links, flight status and environmental threats, and the backup strategy is single and static, unable to adaptively adjust the data protection strength according to the risk evolution trend, which easily leads to resource waste or loss of critical data.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for secure transmission and storage of multi-physical quantity data from a UAV, comprising: The communication link parameters of the UAV are collected, and after being smoothed and filtered in the time domain, they are weighted and fused to obtain the link risk index; Flight status parameters of the UAV are collected, and after validity verification and outlier processing, they are weighted and fused to obtain the flight risk index. The environmental threat index is calculated based on the distance of the drone from the no-fly zone or obstacle. The link risk index, flight risk index and environmental threat index are weighted and integrated to obtain the comprehensive risk index. Record the combined risk index and time interval between the current moment and the previous risk detection moment, and calculate the rate of risk change; A six-level progressive backup urgency level is generated based on the comprehensive risk index and the rate of risk change, and corresponding actions are performed.

[0007] As a preferred solution for the secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs), the following is provided: The collected UAV communication link parameters are then weighted and fused after time-domain smoothing filtering to obtain a link risk index, including: The communication link parameters of the UAV during data transmission are collected, including signal strength, packet loss rate, round-trip delay, and communication module temperature. The collected communication link parameters are processed by time-domain smoothing filtering: a sliding window averaging filtering algorithm is used to process the dynamic response speed and noise characteristics of different parameters.

[0008] As a preferred solution for the secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs), the following is provided: The process of collecting UAV communication link parameters, performing time-domain smoothing filtering, and then weighting and fusing them to obtain the link risk index also includes: During the filtering process, the original data of each sampling point is checked for reasonableness, and extreme outliers that exceed the preset physical reasonable range are removed. When the number of remaining valid data in the current window reaches the preset proportion, the arithmetic mean of the valid data is calculated as the smoothed output at the current moment; otherwise, the smoothed value of the previous moment is used as a substitute. Based on the smoothed and filtered communication link parameters, combined with the preset communication interruption threshold, ideal signal strength reference value, maximum allowable packet loss rate, maximum allowable delay and safe temperature limit, the link risk index is calculated: the signal degradation degree, packet loss risk degree, delay risk degree and communication module temperature risk coefficient are calculated and assigned corresponding weights, and the sum is obtained to obtain the link risk index.

[0009] As a preferred solution for the secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs), the following is provided: The collected UAV flight status parameters, after validity verification and outlier processing, are weighted and fused to obtain a flight risk index, including: Collect flight status parameters of the UAV, including remaining battery power, body vibration amplitude, average motor temperature, and IMU acceleration anomaly. The collected flight status parameters are validated for data validity and outlier handling is performed: upper and lower limits of the physical reasonable range are preset for each parameter. When the real-time measurement value of any parameter exceeds the preset upper or lower limit, it is judged as invalid data and is removed. The value of the previous valid time is used to fill the gap. If invalid data appears for multiple consecutive time points, the current value is predicted based on the previous two valid values ​​using linear extrapolation until the number of consecutive invalid values ​​exceeds the threshold and triggers a system warning.

[0010] As a preferred solution for the secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs), the following is provided: The collected UAV flight status parameters, after validity verification and outlier processing, are weighted and fused to obtain the flight risk index, which also includes: The rate of change of continuously sampled data sequences is checked. The instantaneous rate of change between the current sampled value and the previous valid value is calculated. If the instantaneous rate of change exceeds the preset maximum rate of change threshold, it is judged as an abnormal jump. Instead of directly using the current value, a sliding window median filtering process is used. A window is formed by taking a preset number of sampling points before and after the current time. All data in the window are sorted by numerical value and the median is taken as the valid value at the current time. Additional time window consistency checks are performed to address IMU acceleration anomalies. Based on the flight status parameters after validity verification and outlier processing, combined with the preset minimum power required for safe return, vibration risk threshold, and motor temperature risk threshold, the flight risk index is calculated.

[0011] As a preferred solution for the secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs), the following is provided: The environmental threat index is calculated based on the distance of the drone from the no-fly zone or obstacle. The link risk index, flight risk index, and environmental threat index are then weighted and fused to obtain a comprehensive risk index, including: The link risk index, flight risk index, and environmental threat index are obtained at the current moment. The environmental threat index is calculated based on the measured distance between the drone and the no-fly zone or obstacle. The environmental threat index increases as the measured distance decreases. The minimum value is taken when the measured distance is greater than or equal to the preset safe distance threshold, and the maximum value is taken when the measured distance approaches zero. The link risk index, flight risk index, and environmental threat index are assigned weights and summed to obtain the comprehensive risk index.

[0012] As a preferred solution for the secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs), the following is provided: The process of generating a six-level progressive backup urgency level based on a comprehensive risk index and risk change rate, and performing corresponding operations, includes: The current comprehensive risk index is compared with preset low-risk and high-risk thresholds to determine the basic risk level at the current moment. At the same time, the risk change trend is determined based on the positive or negative or zero risk change rate. The basic risk level and risk change trend are combined to generate a backup urgency assessment result with six progressive levels from low to high. Each level corresponds to preset trigger conditions and execution operations.

[0013] Secondly, the present invention provides a secure transmission and storage system for multi-physical quantity data of unmanned aerial vehicles, comprising: The link risk quantification module is used to collect UAV communication link parameters, which are then weighted and fused after time-domain smoothing filtering to obtain the link risk index. The flight risk analysis module is used to collect UAV flight status parameters, which are then weighted and fused after validity verification and outlier processing to obtain the flight risk index. The comprehensive risk fusion module is used to calculate the environmental threat index based on the distance of the drone from the no-fly zone or obstacle, and to weight and fuse the link risk index, flight risk index and environmental threat index to obtain the comprehensive risk index; The risk trend calculation module is used to record the comprehensive risk index and time interval between the current moment and the previous risk detection moment, and to calculate the risk change rate. The adaptive backup decision module generates a six-level progressive backup urgency based on the comprehensive risk index and the rate of risk change, and performs corresponding operations.

[0014] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for secure transmission and storage of multi-physical quantity data of UAV are implemented.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for secure transmission and storage of multi-physical quantity data of a UAV.

[0016] The beneficial effects of this invention are as follows: By integrating three types of multi-physical quantity data—communication link, flight vehicle, and environmental threats—this invention constructs a comprehensive risk index and risk change rate, enabling dynamic quantification and trend prediction of the overall risk of UAVs. The established six-level progressive backup urgency strategy can adaptively adjust data protection strength according to risk level and evolution direction, avoiding resource waste or critical data loss caused by single static backups. Through the design of hierarchical storage protection zones and an extreme emergency delivery mechanism, the invention can proactively save and broadcast the minimum critical dataset even in extreme scenarios, significantly improving data survivability and flight safety in complex, high-risk environments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an overall flowchart of a method for secure transmission and storage of multi-physical data from unmanned aerial vehicles (UAVs) provided by the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for secure transmission and storage of multi-physical quantity data from a UAV, including: S1: Collect UAV communication link parameters, perform time-domain smoothing filtering, and then weight and fuse them to obtain the link risk index; S2: Collect UAV flight status parameters, perform validity verification and outlier processing, and then weight and fuse them to obtain the flight risk index; S3: Calculate the environmental threat index based on the distance of the drone from the no-fly zone or obstacle, and then weight and fuse the link risk index, flight risk index and environmental threat index to obtain a comprehensive risk index; S4: Record the combined risk index and time interval between the current moment and the previous risk detection moment, and calculate the risk change rate; S5: Generate a six-level progressive backup urgency based on the comprehensive risk index and the rate of risk change, and execute the corresponding operations.

[0021] It should be noted that through steps S1-S5, this invention establishes a complete closed loop from multi-dimensional physical quantity acquisition, risk quantification and fusion, trend prediction to adaptive backup, realizing the overall dynamic risk assessment of UAV communication links, flight body and environmental threats, and automatically matching a six-level progressive data protection strategy according to the risk level and evolution direction, thereby maximizing the secure storage of key data and proactive delivery in extreme scenarios while avoiding resource waste.

[0022] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the previous embodiment, a method for secure transmission and storage of multi-physical quantity data of a UAV is provided, including: In this embodiment, the UAV communication link parameters collected in step S1 above are weighted and fused after time-domain smoothing filtering to obtain the link risk index, which includes: The communication link parameters of the UAV during data transmission are collected. These parameters are used to quantify the real-time quality of the wireless communication link between the UAV and the ground station, providing basic data for subsequent link risk index calculation.

[0023] Specifically, communication link parameters include the following four categories of directly measurable physical quantities: Signal strength, measured in dBm, is read in real time through the received signal strength indicator register of the communication module, reflecting the current channel attenuation level. Packet loss rate, expressed as a percentage, is calculated by the difference between the sequence number of the data packets sent and the sequence number of the acknowledgments received per unit time, and represents the reliability of data transmission. Round-trip delay, measured in milliseconds, is obtained by sending timestamped heartbeat packets and receiving responses, and reflects the real-time response speed of the link. The temperature of the communication module, measured in degrees Celsius, is read directly from the temperature sensor integrated inside the module. It is used to monitor whether the communication hardware is at risk of frequency reduction or disconnection due to overheating. Because of the complex operating environment of UAVs, communication link parameters are susceptible to external interference, resulting in unrealistic and drastic fluctuations. Directly using the raw measurements for risk quantification may cause frequent oscillations in risk levels, reducing the system's decision-making stability. Therefore, before calculating the link risk index, the four types of collected communication link parameters must be subjected to time-domain smoothing filtering.

[0024] Differentiated smoothing processing is performed using a sliding window averaging filter algorithm to address the different physical characteristics of the four types of parameters mentioned above. The size of the sliding window is dynamically set based on the dynamic response speed and noise characteristics of each parameter. For signal strength and round-trip delay, both are highly sensitive to changes in flight attitude and environmental obstruction, and fluctuate at a high frequency. To ensure that the risk index can track rapid changes in link quality in a timely manner, the sliding window length is set to 0.5 seconds. For packet loss rate and communication module temperature, both are slowly varying parameters. Packet loss rate needs to be within a relatively long statistical window to reflect the true channel quality. Communication module temperature changes slowly due to thermal inertia. To avoid excessive smoothing leading to response lag, the sliding window length is set to 2 seconds. During the filtering process, the system first performs a reasonableness check on the original data of each sampling point: if the value exceeds the preset physical reasonable range, it is judged as an extreme outlier and removed; after removal, if the number of valid data remaining in the window is not less than 50% of the total length of the window, the arithmetic mean of these valid data is calculated as the smoothed output at the current moment; if the valid data is less than 50%, the smoothed value of the previous moment is used as a substitute, and a data missing event is recorded in the log.

[0025] The link risk index is obtained by combining the preprocessed communication link parameters with the communication interruption threshold, the ideal signal strength reference value, the maximum allowable packet loss rate, the maximum allowable delay, and the upper limit of safe temperature. The link risk index is calculated using the following formula: in, This is a link risk index. The current signal strength, This is the ideal signal strength reference value. This is the communication interruption threshold. For packet loss rate, The maximum allowable packet loss rate; For round-trip delay, For the maximum allowable delay, Temperature of the communication module; The upper limit of safe temperature; Indicates the degree of signal degradation, when When, the value is 0; when At that time, the value of this item is 1; To assess the risk of packet loss, To mitigate the risk of delay, Temperature risk factor for communication modules; α, β, γ, and δ are weighting coefficients for signal degradation, packet loss risk, latency risk, and communication module temperature risk, respectively, adjusting the importance of communication link parameters to satisfy... The relationship between the weighting coefficients is as follows: .

[0026] It should be noted that signal strength determines whether a basic connection exists in the link and is the most basic guarantee for communication, so signal degradation has the highest weight value; packet loss risk reflects the success rate of data transmission and has a significant impact on critical commands and telemetry data, so it has the second highest weight value; data delay affects the real-time performance of the control loop, but has a smaller impact on non-real-time data transmission, so delay risk has the third highest weight value; the temperature of the communication module changes slowly and will only cause link interruption in extreme cases, serving as an auxiliary monitoring indicator, so the temperature risk coefficient of the communication module has the lowest weight value.

[0027] In this embodiment, the flight status parameters of the UAV collected in step S2 above are weighted and fused after validity verification and outlier processing to obtain the flight risk index, which includes: The flight status parameters of the UAV are collected, and these parameters include the following four types of physical quantities: Remaining battery power The unit is percentage, which is read in real time through the battery management system and reflects the current available energy reserves of the drone. It is a key indicator for determining whether the drone can return safely. The vibration amplitude of the aircraft body, measured in meters per second squared, is calculated by filtering the data from the triaxial accelerometer in the inertial measurement unit and then synthesizing the amplitude. It is used to characterize the balance of the propeller blades, the wear of the motor bearings, and the overall structural health. The average motor temperature, in degrees Celsius, is obtained by taking the arithmetic average of the temperature readings from the temperature sensors inside each motor or integrated into the electronic speed controller. It is used to monitor the thermal load of the power system and prevent motor demagnetization or power attenuation due to overheating. The IMU acceleration anomaly is a dimensionless statistic calculated by the percentage of time within a statistical unit time window when the rate of change of acceleration exceeds a preset threshold. This parameter is used to quantify the precursor risk of attitude loss of control. When the drone exhibits severe shaking or a loss of control trend, this value increases significantly.

[0028] The system performs data validity verification and outlier handling for flight status parameters. For four types of parameters—remaining battery power, airframe vibration amplitude, average motor temperature, and IMU acceleration anomaly—upper and lower limits of their physical reasonable ranges are preset. When the real-time measured value of any parameter exceeds its preset upper or lower limit, the data is determined to be invalid and discarded, and replaced with the value from the previous valid time point. If invalid data appears for multiple consecutive time points, a linear extrapolation method is used to predict the current value based on the previous two valid values, until the number of consecutive invalid values ​​exceeds a threshold, triggering a system warning. A rate of change check is performed on the continuously sampled data sequence. The absolute difference between the current sampled value and the previous valid value is calculated and divided by the sampling interval to obtain the instantaneous rate of change. If this rate of change exceeds a preset maximum rate of change threshold, it is determined to be an abnormal jump, and the current value is not directly adopted. For this abnormal value, a sliding window median filtering process is used: a window is formed by taking N sampling points before and after the current time, where N is 3 to 5. All data within the window are sorted by value, and the median is taken as the valid value at the current time. Compared with mean filtering, median filtering can better preserve signal edge features and effectively suppress isolated impulse noise. Since the IMU acceleration anomaly rate is an indicator calculated based on a statistical window, its validity depends on the integrity of the original data within the window. During the verification process, an additional time window consistency check is introduced: the percentage of valid data in the original acceleration data within the unit time window used to calculate the anomaly rate is counted. If the percentage of valid data is not less than 80%, the calculated anomaly rate value is accepted. If it is less than 80%, the statistical window is extended to 1.5 seconds or 2 seconds, and the calculation is repeated until the percentage of valid data meets the standard. If it still does not meet the standard after the extension, the anomaly rate at that moment is marked as invalid, and the previous valid anomaly rate value is used instead.

[0029] The flight risk index is obtained by combining flight status parameters with the minimum power required for safe return, vibration risk threshold, and motor temperature risk threshold. The flight risk index is calculated using the following formula: in, Flight risk index; Contribution value to vibration risk The amplitude of the body vibration. Vibration risk threshold; Contribution value to temperature risk This represents the average temperature of the motor. This is the motor temperature risk threshold; For IMU acceleration anomaly; Contribution value to electricity risk B represents the current remaining battery power. Minimum battery power required for a safe return; ϵ,ζ,η,θ are the weighting coefficients for the electrical risk contribution, vibration risk contribution, temperature risk contribution, and IMU acceleration anomaly, respectively, satisfying the following conditions: The relationship between the weighting coefficients is as follows: .

[0030] It should be noted that insufficient battery power is the most common cause of drone crashes or loss of contact, directly affecting the ability to return to base, so it is given the highest weight for battery power risk contribution; abnormal IMU acceleration directly reflects the precursor to attitude loss of control and is crucial to flight stability, so it is given the second highest weight; abnormal vibration of the airframe indicates propeller imbalance, bearing wear, or structural loosening, and is a major precursor to mechanical failure, but its changes are usually gradual and not a sudden risk, so it is given the third highest weight for vibration risk contribution; motor temperature usually changes slowly and only becomes a significant risk under prolonged high load or high temperature conditions, so it is given the lowest weight for temperature risk contribution.

[0031] In this embodiment, in step S3 above, the environmental threat index is calculated based on the distance of the drone from the no-fly zone or obstacle. The link risk index, flight risk index, and environmental threat index are then weighted and fused to obtain a comprehensive risk index, which includes: The current link risk index, the current flight risk index, and the environmental threat index are weighted and fused to obtain a comprehensive risk index, which is calculated using the following formula: in, This is a comprehensive risk index; As an environmental threat index, , where d is the measured distance of the drone relative to the no-fly zone and obstacles, in meters, obtained by fusing a geofence database with an airborne obstacle avoidance sensor; The safe distance threshold, measured in meters, is set based on the drone's speed, braking performance, and mission requirements. When the real-time distance between the drone and a no-fly zone or obstacle is greater than or equal to the safe distance threshold, the environmental threat index is... This indicates that there is currently no external environmental threat; when the drone enters the danger zone, that is... Less than When the distance is zero, the environmental threat index increases linearly with decreasing distance; when the distance of the drone is zero, , indicating that the environmental threat has reached its maximum value; λ,μ,y are the weighting coefficients of the link risk index, flight risk index, and environmental threat index, respectively, satisfying .

[0032] In this embodiment, the process of recording the combined risk index and time interval between the current moment and the previous risk detection moment in step S4, and calculating the risk change rate includes: The time interval for each risk detection is recorded, and the rate of risk change is obtained by combining the current comprehensive risk index with the previous comprehensive risk index. The rate of risk change is calculated using the following formula: in: For the rate of change of risk; This represents the overall risk index at the current moment. This is the previous comprehensive risk index; This represents the time interval between two tests.

[0033] It should be noted that the current comprehensive risk index is used to quantify the real-time overall risk level of UAVs across three dimensions: communication links, flight operations, and environmental threats. It serves as a fundamental indicator for classifying UAVs into low-risk, medium-risk, and high-risk levels. This index is obtained by weighted fusion of the link risk index, flight risk index, and environmental threat index, with a value range of 0 to 1. A higher value indicates a higher overall risk currently faced by the UAV. Based on a comparison of this index with preset low-risk and high-risk thresholds, the system classifies the real-time risk status into three basic levels: low-risk, medium-risk, and high-risk, providing a basis for subsequent backup decisions.

[0034] It should also be noted that the rate of risk change characterizes the trend and speed of change of the comprehensive risk index over time, reflecting whether the risk is worsening, remaining stable, or gradually improving. The rate of risk change is calculated by dividing the difference between the comprehensive risk index at the current moment and the comprehensive risk index at the previous moment by the sampling interval, and its unit is per second. When the rate of risk change is positive, it indicates that the risk is on an upward trend, and the larger the value, the faster the deterioration; when the rate is negative, it indicates that the risk is on a downward trend and the situation is improving; when the rate is zero, it indicates that the risk remains relatively stable.

[0035] In this embodiment, step S5 above, which generates a six-level progressive backup urgency based on the comprehensive risk index and the rate of risk change, and performs corresponding operations, includes: The current comprehensive risk index is compared with preset low-risk and high-risk thresholds to generate the basic risk level at the current moment. Simultaneously, the rate of risk change is introduced as a dynamic judgment criterion to generate a risk change trend. Based on this, a backup urgency assessment result is generated. The backup urgency is divided into six progressive levels from low to high according to the difference between the basic risk level and the change trend. Each level corresponds to a specific trigger condition and execution operation. The first level is regular storage, which is triggered when the current comprehensive risk index is lower than the low risk threshold and the risk change rate is not greater than zero, that is, the risk is low and the change trend is stable or decreasing. Under this level, the system does not trigger any active backup operations. Data is written to the regular area of ​​the storage medium according to the regular strategy and uploaded to the ground station or cloud according to the preset cycle. The second level is enhanced monitoring, triggered when the current comprehensive risk index is below the low-risk threshold but the rate of risk change is greater than zero, meaning the risk is low but the trend is upward. At this level, the system does not trigger actual data backup operations, but increases the detection frequency of the comprehensive risk index to twice the normal frequency, and records the monitoring status in the metadata log area to promptly detect early signs of risk deterioration. The third level is light compression backup, which is triggered when the current comprehensive risk index is in the low-to-medium range between the low-risk and high-risk thresholds, and the risk change rate is not greater than zero, that is, the risk is in the low-to-medium range and the change trend is stable. At this level, the system starts light degradation backup, uses lossless compression algorithm to reduce the bit rate of the original video stream, uses LZ4 compression for time-series physical quantities, and writes the compressed data to the reserved protection area of ​​the storage medium, which must not be covered by regular data. Level 4 is summary backup, which is triggered when the current comprehensive risk index is in the middle-high range between the low-risk threshold and the high-risk threshold, and the risk change rate is greater than zero, that is, the risk is in the middle-high range and the change trend is upward. At this level, the system performs moderate degradation backup, stops continuous video recording, and instead extracts one key frame per second and saves it in JPEG format. At the same time, the physical quantity sampling rate is reduced to 50% of the original, and the degraded data is written to the reserved protection area. Level 5 is feature backup, which is triggered when the current comprehensive risk index reaches or exceeds the high-risk threshold and the risk change rate is not greater than zero, that is, the risk is high but the change trend is stable or decreasing. At this level, the system performs a severe degradation backup, no longer saving the original images or videos, but only saving the feature vectors output by the airborne artificial intelligence model, while retaining the high-frequency key physical quantity data of the last ten seconds, writing it to the emergency protection zone of the storage medium, and automatically enabling hardware write protection after the write is completed to prevent any overwrite operation. Level 6 is extreme emergency delivery, triggered when the current comprehensive risk index reaches or exceeds the high-risk threshold and the risk change rate is greater than zero, meaning the risk is high and the trend continues to worsen. At this level, the system performs extreme degradation backup, storing only the minimum critical dataset locally, writing it to the emergency protection zone and enabling hardware write protection; simultaneously, all non-critical communication tasks are interrupted, and the aforementioned minimum critical dataset is broadcast to all reachable external receivers through redundant encoding, achieving proactive data delivery in emergency situations. The backup urgency assessment results of the above six levels, along with their corresponding triggering conditions and execution operations, are all recorded in real time in the metadata log area of ​​the storage medium, forming a complete decision traceability chain.

[0036] Example 3: The above is an illustrative scheme of a method for secure transmission and storage of multi-physical quantity data from a UAV according to this embodiment. It should be noted that the technical solution of a secure transmission and storage system for multi-physical quantity data from a UAV and the technical solution of the aforementioned method for secure transmission and storage of multi-physical quantity data from a UAV belong to the same concept. Details not described in detail in the technical solution of the secure transmission and storage system for multi-physical quantity data from a UAV in this embodiment can be found in the description of the technical solution of the aforementioned method for secure transmission and storage of multi-physical quantity data from a UAV.

[0037] This embodiment also provides a secure transmission and storage system for multi-physical quantity data from unmanned aerial vehicles (UAVs), including: The link risk quantification module is used to collect UAV communication link parameters, which are then weighted and fused after time-domain smoothing filtering to obtain the link risk index. The flight risk analysis module is used to collect UAV flight status parameters, which are then weighted and fused after validity verification and outlier processing to obtain the flight risk index. The comprehensive risk fusion module is used to calculate the environmental threat index based on the distance of the drone from the no-fly zone or obstacle, and to weight and fuse the link risk index, flight risk index and environmental threat index to obtain the comprehensive risk index; The risk trend calculation module is used to record the comprehensive risk index and time interval between the current moment and the previous risk detection moment, and to calculate the risk change rate. The adaptive backup decision module generates a six-level progressive backup urgency based on the comprehensive risk index and the rate of risk change, and performs corresponding operations.

[0038] This embodiment also provides an electronic device applicable to a method for secure transmission and storage of multi-physical quantity data from a UAV, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a secure transmission and storage method for multi-physical quantity data of a UAV as proposed in the above embodiments.

[0039] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a method for secure transmission and storage of UAV multi-physical quantity data as proposed in the above embodiment.

[0040] The storage medium proposed in this embodiment belongs to the same inventive concept as the secure transmission and storage method for multi-physical quantity data of UAV proposed in the above embodiment. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0041] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for secure transmission and storage of multi-physical quantity data from unmanned aerial vehicles (UAVs), characterized in that, include: The communication link parameters of the UAV are collected, and after being smoothed and filtered in the time domain, they are weighted and fused to obtain the link risk index; Flight status parameters of the UAV are collected, and after validity verification and outlier processing, they are weighted and fused to obtain the flight risk index. The environmental threat index is calculated based on the distance of the drone from the no-fly zone or obstacle. The link risk index, flight risk index and environmental threat index are weighted and integrated to obtain the comprehensive risk index. Record the combined risk index and time interval between the current moment and the previous risk detection moment, and calculate the rate of risk change; A six-level progressive backup urgency level is generated based on the comprehensive risk index and the rate of risk change, and corresponding actions are performed.

2. The method for secure transmission and storage of multi-physical quantity data from a UAV as described in claim 1, characterized in that, The collected UAV communication link parameters are then weighted and fused after time-domain smoothing filtering to obtain a link risk index, including: The communication link parameters of the UAV during data transmission are collected, including signal strength, packet loss rate, round-trip delay, and communication module temperature. The collected communication link parameters are processed by time-domain smoothing filtering: a sliding window averaging filtering algorithm is used to process the dynamic response speed and noise characteristics of different parameters.

3. The method for secure transmission and storage of multi-physical quantity data from a UAV as described in claim 2, characterized in that, The process of collecting UAV communication link parameters, performing time-domain smoothing filtering, and then weighting and fusing them to obtain the link risk index also includes: During the filtering process, the original data of each sampling point is checked for reasonableness, and extreme outliers that exceed the preset physical reasonable range are removed. When the number of remaining valid data in the current window reaches the preset proportion, the arithmetic mean of the valid data is calculated as the smoothed output at the current moment; otherwise, the smoothed value of the previous moment is used as a substitute. Based on the smoothed and filtered communication link parameters, combined with the preset communication interruption threshold, ideal signal strength reference value, maximum allowable packet loss rate, maximum allowable delay and safe temperature limit, the link risk index is calculated: the signal degradation degree, packet loss risk degree, delay risk degree and communication module temperature risk coefficient are calculated and assigned corresponding weights, and the sum is obtained to obtain the link risk index.

4. The method for secure transmission and storage of multi-physical quantity data from a UAV as described in claim 3, characterized in that, The collected UAV flight status parameters, after validity verification and outlier processing, are weighted and fused to obtain a flight risk index, including: Collect flight status parameters of the UAV, including remaining battery power, body vibration amplitude, average motor temperature, and IMU acceleration anomaly. The collected flight status parameters are validated for data validity and outlier handling is performed: upper and lower limits of the physical reasonable range are preset for each parameter. When the real-time measurement value of any parameter exceeds the preset upper or lower limit, it is judged as invalid data and is removed. The value of the previous valid time is used to fill the gap. If invalid data appears for multiple consecutive time points, the current value is predicted based on the previous two valid values ​​using linear extrapolation until the number of consecutive invalid values ​​exceeds the threshold and triggers a system warning.

5. A method for secure transmission and storage of multi-physical quantity data from a UAV as described in claim 4, characterized in that, The collected UAV flight status parameters, after validity verification and outlier processing, are weighted and fused to obtain the flight risk index, which also includes: The rate of change of continuously sampled data sequences is checked. The instantaneous rate of change between the current sampled value and the previous valid value is calculated. If the instantaneous rate of change exceeds the preset maximum rate of change threshold, it is judged as an abnormal jump. Instead of directly using the current value, a sliding window median filtering process is used. A window is formed by taking a preset number of sampling points before and after the current time. All data in the window are sorted by numerical value and the median is taken as the valid value at the current time. Additional time window consistency checks are performed to address IMU acceleration anomalies. Based on the flight status parameters after validity verification and outlier processing, combined with the preset minimum power required for safe return, vibration risk threshold, and motor temperature risk threshold, the flight risk index is calculated.

6. A method for secure transmission and storage of multi-physical quantity data from a UAV as described in claim 5, characterized in that, The environmental threat index is calculated based on the distance of the drone from the no-fly zone or obstacle. The link risk index, flight risk index, and environmental threat index are then weighted and fused to obtain a comprehensive risk index, including: The link risk index, flight risk index, and environmental threat index are obtained at the current moment. The environmental threat index is calculated based on the measured distance between the drone and the no-fly zone or obstacle. The environmental threat index increases as the measured distance decreases. The minimum value is taken when the measured distance is greater than or equal to the preset safe distance threshold, and the maximum value is taken when the measured distance approaches zero. The link risk index, flight risk index, and environmental threat index are assigned weights and summed to obtain the comprehensive risk index.

7. A method for secure transmission and storage of multi-physical quantity data from a UAV as described in claim 6, characterized in that, The process of generating a six-level progressive backup urgency level based on a comprehensive risk index and risk change rate, and performing corresponding operations, includes: The current comprehensive risk index is compared with preset low-risk and high-risk thresholds to determine the basic risk level at the current moment. At the same time, the risk change trend is determined based on the positive or negative or zero risk change rate. The basic risk level and risk change trend are combined to generate a backup urgency assessment result with six progressive levels from low to high. Each level corresponds to preset trigger conditions and execution operations.

8. A secure transmission and storage system for multi-physical quantity data from unmanned aerial vehicles (UAVs), employing the method described in any one of claims 1 to 7, characterized in that, include: The link risk quantification module is used to collect UAV communication link parameters, which are then weighted and fused after time-domain smoothing filtering to obtain the link risk index. The flight risk analysis module is used to collect UAV flight status parameters, which are then weighted and fused after validity verification and outlier processing to obtain the flight risk index. The comprehensive risk fusion module is used to calculate the environmental threat index based on the distance of the drone from the no-fly zone or obstacle, and to weight and fuse the link risk index, flight risk index and environmental threat index to obtain the comprehensive risk index; The risk trend calculation module is used to record the comprehensive risk index and time interval between the current moment and the previous risk detection moment, and to calculate the risk change rate. The adaptive backup decision module generates a six-level progressive backup urgency based on the comprehensive risk index and the rate of risk change, and performs corresponding operations.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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