A method and device for protecting electronic circuits of a drone

Abstract

The application provides a kind of unmanned plane electronic circuit protection method and device, it is related to unmanned plane technical field, the method includes: by multimodal sensor real-time acquisition unmanned plane the electromagnetic field intensity data and temperature data of environment where it is located;Based on electromagnetic field intensity data, using wavelet threshold denoising algorithm to filter processing on-board circuit signal, obtain filtered circuit signal;The distribution position of multimodal sensor in three-dimensional space is fused with electromagnetic field intensity data and temperature data, and spatial environment correlation data is obtained;Based on spatial environment correlation data and filtered circuit signal, using pyramid volume algorithm calculates the spatio-temporal distribution intensity of current electromagnetic environment interference, to construct dynamic interference weight.The application solves the problem that the existing unmanned plane circuit protection technology has weak anti-interference ability, low heat dissipation efficiency, abnormal response lag and rigid protection strategy in complex extreme environment, realizes the multi-level real-time effective protection of unmanned plane electronic circuit system.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and device for protecting the electronic circuits of UAVs. Background Technology

[0002] With the widespread application of drone technology in complex scenarios such as power line inspection and industrial monitoring, its electronic circuit system faces multiple threats from extreme environments. When faced with strong electric and magnetic environments, high temperature environments, and high power operation of equipment, crashes often occur, causing equipment damage and interruption of inspection tasks.

[0003] Existing UAV circuit protection technologies mostly employ a single-dimensional passive defense strategy, which is insufficient to cope with complex operating conditions involving multi-physical field coupling. Firstly, electromagnetic protection is passive, and the coordination of heat dissipation systems is poor. For example, existing technologies often rely on fixed shielding structures or static filtering algorithms, which cannot dynamically adjust noise reduction strategies based on real-time electromagnetic field strength. This results in a signal-to-noise ratio drop of more than half under strong electromagnetic environments, leading to large delays and deviations in control commands. Single-air cooling significantly reduces heat dissipation efficiency under high temperatures, while fan vibration can interfere with precision sensors. Secondly, there is the problem of isolated abnormal response mechanisms and static protection strategies. For example, each system operates independently, lacking multi-parameter fusion judgment. When the temperature exceeds the threshold, non-core loads are not cut off in time, and redundant motors cannot start synchronously, missing the fault recovery window. The parameters of electromagnetic shielding, heat dissipation control, and structural protection are fixed and cannot be adaptively adjusted based on environmental threat assessment results, greatly increasing the probability of protection failure in complex interference scenarios. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for protecting the electronic circuits of unmanned aerial vehicles (UAVs), which solves the problems of weak anti-interference ability, low heat dissipation efficiency, delayed abnormal response and rigid protection strategy of existing UAV circuit protection technology in complex and extreme environments, and realizes multi-level real-time effective protection of UAV electronic circuit systems.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for protecting the electronic circuits of a drone, the method comprising:

[0007] The electromagnetic field strength and temperature data of the environment in which the drone is located are collected in real time by multimodal sensors.

[0008] Based on electromagnetic field strength data, a wavelet threshold noise reduction algorithm is used to filter the airborne circuit signal to obtain the filtered circuit signal.

[0009] By fusing the distribution of multimodal sensors in three-dimensional space with electromagnetic field intensity data and temperature data, spatial environment correlation data can be obtained.

[0010] Based on spatial environment correlation data and filtered circuit signals, the pyramidal volume algorithm is used to calculate the spatiotemporal distribution intensity of current electromagnetic interference in order to construct dynamic interference weights.

[0011] Based on the dynamic interference weight, low-frequency electromagnetic interference is compensated in real time to obtain the circuit signal state after anti-interference preprocessing.

[0012] Based on the circuit signal state, temperature data, and dynamic interference weights after anti-interference preprocessing, temperature control commands are obtained, and the circuit operating frequency and power are dynamically adjusted according to the temperature control commands.

[0013] During the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected based on the inertial sensor, a signal protection mechanism is triggered to reduce the power supply priority of non-critical circuits. Then, the backup signal path is started and the operating status of the backup signal path is recorded.

[0014] Based on the operating status of the backup signal path, current monitoring data, and dynamic interference weights, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy.

[0015] Based on the adjusted signal processing strategy, the current critical data stream is obtained, and the critical data stream is isolated and prioritized.

[0016] Furthermore, based on the electromagnetic field strength data, a wavelet threshold denoising algorithm is used to filter the airborne circuit signals, resulting in filtered circuit signals, including:

[0017] Based on electromagnetic field strength data, interference characteristics in airborne circuit signals are obtained;

[0018] Based on the interference characteristics, wavelet basis functions and threshold functions are used to perform wavelet decomposition, thresholding, and wavelet reconstruction on the airborne circuit signal to obtain the filtered circuit signal.

[0019] Furthermore, the distribution locations of the multimodal sensors in three-dimensional space are fused with electromagnetic field intensity data and temperature data to obtain spatial environment-related data, including:

[0020] Acquire the distribution and location information of multimodal sensors in the three-dimensional space of the UAV;

[0021] The distribution location information is fused with the electromagnetic field strength data and temperature data at the corresponding locations to obtain the fused data;

[0022] Based on the fused data, spatial environmental association data is obtained to characterize the distribution of environmental parameters in three-dimensional space.

[0023] Furthermore, based on spatial environment correlation data and filtered circuit signals, the pyramidal volume algorithm is used to calculate the spatiotemporal distribution intensity of current electromagnetic interference in order to construct dynamic interference weights, including:

[0024] Based on spatial environment correlation data, the distribution characteristics of electromagnetic field and temperature in three-dimensional space are extracted, where the distribution characteristics are composed of multiple sensor data points;

[0025] Based on the filtered circuit signal, key data points are selected as the vertices of the pyramid from multiple sensor data points contained in the distribution characteristics.

[0026] An irregular pyramid is constructed based on the vertices of the pyramid, and the spatiotemporal distribution intensity of electromagnetic environmental interference is characterized by calculating the geometric volume of the irregular pyramid.

[0027] Based on the spatiotemporal distribution intensity, a dynamic interference weight is obtained to quantify the degree of interference.

[0028] Furthermore, based on dynamic interference weights, low-frequency electromagnetic interference is compensated in real time to obtain the circuit signal state after anti-interference preprocessing, including:

[0029] Based on the dynamic interference weight, an inverse compensation signal with the opposite phase and corresponding amplitude to the low-frequency electromagnetic interference is obtained;

[0030] The inverse compensation signal is superimposed on the filtered circuit signal in real time to obtain the compensated circuit signal.

[0031] Based on the compensation of the circuit signal to cancel out the low-frequency electromagnetic interference components, the circuit signal state after anti-interference preprocessing is obtained.

[0032] Furthermore, based on the circuit signal state, temperature data, and dynamic interference weights after anti-interference preprocessing, a temperature control command is obtained. The circuit operating frequency and power are then dynamically adjusted according to the temperature control command, including:

[0033] The dynamic disturbance weights are matched with the preset disturbance heat load mapping table to obtain the corresponding basic heat load coefficients.

[0034] The basic heat load coefficient is dynamically corrected, and the real-time heat load index is obtained based on the rate of change of signal amplitude in the circuit signal state after anti-interference preprocessing.

[0035] The real-time heat load index and temperature data are weighted and fused to obtain a comprehensive heat load assessment value;

[0036] Based on the preset threshold range to which the comprehensive heat load assessment value belongs, a quantitative temperature control command including frequency adjustment parameters and power adjustment parameters is obtained;

[0037] According to the quantized temperature control command, the operating frequency and power supply of the airborne circuits are reduced synchronously to achieve heat dissipation balance.

[0038] Furthermore, during the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected based on the inertial sensor, a signal protection mechanism is triggered, reducing the power supply priority of non-critical circuits. Then, a backup signal path is activated, and the operating status of the backup signal path is recorded, including:

[0039] During the dynamic adjustment process, vibration data is continuously collected by an inertial sensor, and the time-domain and frequency-domain features of the vibration data are extracted simultaneously.

[0040] The time-domain and frequency-domain characteristics of vibration data are displayed and analyzed. When the characteristic value exceeds the preset safety threshold, it is determined to be an abnormal vibration signal.

[0041] A trigger command is received based on the abnormal vibration signal, and the signal protection mechanism is activated simultaneously.

[0042] Based on the trigger command, power supply resources are reallocated, and the power supply priority of non-critical circuits is reduced.

[0043] Based on the reduction of power supply priority, the pre-configured backup signal path is activated;

[0044] The operation status of the backup signal path is monitored and recorded in real time, including signal transmission quality and stability parameters.

[0045] Furthermore, based on the operating status of the backup signal path, current monitoring data, and dynamic interference weights, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy, including:

[0046] Extract the signal transmission quality and stability parameters of the backup signal path;

[0047] By fusing and analyzing signal transmission quality parameters, stability parameters, and electromagnetic field strength data from current monitoring data, the overall quality level of the current signal transmission environment can be assessed.

[0048] The overall quality level is weighted and corrected, and the environmental adaptability assessment result is obtained based on the dynamic disturbance weight.

[0049] Based on the environmental adaptability assessment results, the corresponding signal modulation parameters, coding rate parameters, and retransmission mechanism parameters are adaptively selected from the preset strategy library;

[0050] By integrating the signal modulation parameters, coding rate parameters, and retransmission mechanism parameters, the adjusted signal processing strategy is obtained.

[0051] Furthermore, based on the adjusted signal processing strategy, the current critical data stream is obtained, and the critical data stream is isolated and prioritized, including:

[0052] Acquire a mixed data stream containing multiple data types output by the drone;

[0053] Based on the signal modulation parameters and coding rate parameters, the mixed data stream is analyzed and its features are identified to obtain the identified data features;

[0054] Based on the identified data features, flight control data and navigation and positioning data are extracted from the mixed data stream as key data streams;

[0055] The critical data stream is transmitted through a dedicated physical channel and encapsulated to achieve physical isolation from other data streams, resulting in an isolated critical data stream.

[0056] Real-time priority coefficients are calculated for isolated critical data streams, and the calculated real-time priority coefficients are obtained based on the dynamic interference weight and environmental adaptability assessment results.

[0057] Based on the calculated real-time priority coefficients, corresponding bandwidth resources and transmission time slots are allocated to different categories of isolated critical data streams to obtain the final scheduling instructions, thereby completing the isolation and priority scheduling of critical data streams.

[0058] Secondly, a protective device for the electronic circuits of a drone includes:

[0059] The acquisition module is used to collect electromagnetic field strength data and temperature data of the environment in which the UAV is located in real time through multimodal sensors;

[0060] The calculation module uses wavelet threshold noise reduction algorithm to filter the airborne circuit signals based on electromagnetic field strength data to obtain filtered circuit signals; it also fuses the distribution of multimodal sensors in three-dimensional space with electromagnetic field strength data and temperature data to obtain space environment correlation data.

[0061] The module constructs dynamic interference weights by using a pyramidal volume algorithm to calculate the spatiotemporal distribution intensity of current electromagnetic interference based on spatial environment correlation data and filtered circuit signals.

[0062] The processing module performs real-time compensation processing on low-frequency electromagnetic interference based on dynamic interference weights to obtain the circuit signal state after anti-interference preprocessing.

[0063] The adjustment module, based on the circuit signal state after anti-interference preprocessing, temperature data, and dynamic interference weights, obtains temperature control commands and dynamically adjusts the circuit operating frequency and power according to the temperature control commands. During the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected by the inertial sensor, a signal protection mechanism is triggered, reducing the power supply priority of non-critical circuits. Then, a backup signal path is started, and the operating status of the backup signal path is recorded. Based on the operating status of the backup signal path, current monitoring data, and dynamic interference weights, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy.

[0064] The scheduling module, based on the adjusted signal processing strategy, obtains the current critical data stream and isolates and prioritizes the critical data stream.

[0065] The above-described solution of the present invention has at least the following beneficial effects:

[0066] Because it employs real-time acquisition of multimodal sensors and fusion technology of spatial environment data, it overcomes the problem that single-dimensional passive defense cannot cope with the coupling of multiple physical fields, thus realizing comprehensive perception and correlation analysis of environmental parameters; because it adopts wavelet threshold denoising and dynamic interference weight construction, combined with real-time compensation for low-frequency electromagnetic interference, it overcomes the problems of low signal-to-noise ratio and large control delay in strong electromagnetic environments, thus improving the anti-interference capability of circuit signals; because it adopts dynamic thermal load assessment and circuit frequency and power linkage adjustment, it overcomes the problem of low single heat dissipation efficiency, thus achieving heat dissipation balance in high-temperature environments; because it adopts a multi-level protection mechanism triggered by abnormal vibration and backup path switching, it overcomes the problem of abnormal response lag, thus shortening the fault recovery window period; because it adopts adaptive adjustment of signal processing strategy and isolation scheduling of key data streams, it overcomes the problem of static rigidity of protection strategy, thus enhancing the stability and task continuity of the system in complex interference scenarios. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating a method for protecting the electronic circuits of a drone, as provided in an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram of an electronic circuit protection device for a drone provided by an embodiment of the present invention. Detailed Implementation

[0069] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0070] like Figure 1 As shown, an embodiment of the present invention proposes a method for protecting the electronic circuits of a drone, the method comprising the following steps:

[0071] Step 1: Collect electromagnetic field strength and temperature data of the environment in which the UAV is located in real time using multimodal sensors;

[0072] Step 2: Based on the electromagnetic field strength data, the airborne circuit signal is filtered using a wavelet threshold noise reduction algorithm to obtain the filtered circuit signal.

[0073] Step 3: The distribution location of the multimodal sensors in three-dimensional space is fused with electromagnetic field intensity data and temperature data to obtain spatial environment correlation data;

[0074] Step 4: Based on the spatial environment correlation data and the filtered circuit signal, the pyramidal volume algorithm is used to calculate the spatiotemporal distribution intensity of the current electromagnetic environment interference in order to construct dynamic interference weights.

[0075] Step 5: Based on the dynamic interference weight, perform real-time compensation processing on low-frequency electromagnetic interference to obtain the circuit signal state after anti-interference preprocessing.

[0076] Step 6: Based on the circuit signal state, temperature data and dynamic interference weights after anti-interference preprocessing, obtain the temperature control command, and dynamically adjust the circuit operating frequency and power according to the temperature control command;

[0077] Step 7: During the dynamic adjustment process, real-time monitoring data is collected. If an abnormal vibration signal is detected based on the inertial sensor, a signal protection mechanism is triggered to reduce the power supply priority of non-critical circuits. Then, the backup signal path is started and the operating status of the backup signal path is recorded.

[0078] Step 8: Based on the operating status of the backup signal path, the current monitoring data, and the dynamic interference weight, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy.

[0079] Step 9: Based on the adjusted signal processing strategy, obtain the current critical data stream and isolate and prioritize the critical data stream.

[0080] In this embodiment of the invention, because multimodal sensors are used to collect environmental data in real time, the problem of incomplete environmental perception is overcome, thereby achieving comprehensive real-time monitoring of electromagnetic fields and temperature; because wavelet threshold denoising algorithm filtering is used, the problem of large signal interference in strong electromagnetic environments is overcome, resulting in cleaner circuit signals; because sensor spatial location and environmental data are fused, the problem of lack of spatial correlation analysis of environmental parameters is overcome, thereby forming comprehensive spatial environmental correlation data; because a pyramidal volume algorithm is used to construct dynamic interference weights, the problem of static electromagnetic interference assessment is overcome, thereby accurately quantifying the spatiotemporal distribution of interference; because low-frequency electromagnetic compensation is performed based on dynamic weights, the problem of... The problem of low-frequency interference being difficult to eliminate is addressed, thereby improving the anti-interference capability of circuit signals; by combining multi-parameter temperature control commands to dynamically adjust the circuit operating state, the problem of low efficiency of single heat dissipation is overcome, thus achieving heat dissipation balance in high-temperature environments; by triggering a protection mechanism and activating a backup path through abnormal vibration, the problem of delayed abnormal response is overcome, thus enabling rapid response to faults and reducing losses; by adaptively adjusting signal processing strategies based on multiple factors, the problem of static and rigid protection strategies is overcome, thus optimizing signal processing to adapt to complex environments; by isolating and prioritizing critical data streams, the problem of critical data being susceptible to interference is overcome, thus ensuring the stability and timeliness of core data transmission.

[0081] In a preferred embodiment of the present invention, step 1 above may include:

[0082] Step 1.1: Configure a multimodal sensor group containing electromagnetic sensors and temperature sensors. The electromagnetic sensors collect raw data on the intensity of the ambient electromagnetic field, and the temperature sensors collect raw data on the temperature of the airborne circuit and the surrounding environment. Specifically, based on the strong electric and magnetic field threats and high temperature threats faced by the electronic circuits of UAVs in complex scenarios such as power line inspection and industrial monitoring, configure a multimodal sensor group consisting of electromagnetic sensors and temperature sensors, and deploy them in the core area of ​​the airborne circuit, the heat source area of ​​the fuselage, and different positions of the fuselage. The two types of sensors collect raw data on the intensity of the ambient electromagnetic field and raw data on the temperature of the airborne circuit and the surrounding environment in real time.

[0083] Step 1.2: Control the sensor group to synchronously collect the above raw data and add a unified timestamp to ensure spatiotemporal correlation. Specifically, this includes: controlling the multimodal sensor group to synchronously collect data at a preset sampling frequency. The sampling frequency is adjusted according to the task type. When power inspection faces strong electromagnetic interference, a higher frequency is set, and when the industrial monitoring environment is stable, the frequency is appropriately reduced. During the collection process, relying on the airborne unified clock unit, the same timestamp is added to the raw electromagnetic field strength data and raw temperature data collected simultaneously by each group to ensure that the two types of data are completely corresponding in the time dimension, and to avoid deviations in subsequent analysis and protection adjustments.

[0084] Step 1.3 involves preliminary noise reduction and outlier removal of the timestamped raw data to obtain an initial dataset containing electromagnetic field strength and temperature data. Specifically, this includes: addressing the issues of decreased signal-to-noise ratio under strong electromagnetic environments and abnormal data jumps in sensor data under high temperatures, preliminary noise reduction processing is performed on the raw data with uniform timestamping: in power inspection scenarios, 50 Hz power frequency interference is filtered out, and in industrial monitoring scenarios, specific frequency interference from equipment is filtered out; then, thresholds are set based on the reasonable range of electromagnetic field strength and temperature during normal operation of the UAV, and values ​​exceeding the thresholds are identified as outliers and removed, ultimately obtaining the initial dataset containing accurate electromagnetic field strength and temperature data.

[0085] In this embodiment of the invention, because a multimodal sensor group containing electromagnetic and temperature sensors is configured to collect raw data on the environmental electromagnetic field strength and the onboard circuit and surrounding temperature, the problem of limited data collection dimensions of a single sensor is overcome. This allows for the comprehensive capture of data on both electromagnetic and high-temperature threats, providing complete basic data for subsequent protection. Furthermore, because the sensor group synchronously collects raw data and adds a unified timestamp, the problems of data asynchrony and poor spatiotemporal correlation are overcome, ensuring accurate time correspondence between the two types of data and preventing deviations from affecting subsequent analysis and adjustments. Finally, because the timestamped raw data undergoes preliminary noise reduction and outlier removal, the problem of low data accuracy under strong electromagnetic and high-temperature environments is overcome, resulting in accurate data and a reliable initial dataset, providing high-quality input for subsequent steps.

[0086] In a preferred embodiment of the present invention, step 2 above may include:

[0087] Step 2.1: Based on electromagnetic field strength data, obtain the interference characteristics in the airborne circuit signal. Specifically, this includes: first, calling up the electromagnetic field strength data and performing time-domain and frequency-domain analysis on the data; by observing the amplitude of the numerical fluctuations, the frequency of change, and the energy distribution in a specific frequency range of the electromagnetic field strength at different time periods, distinguishing the main types of interference in the current scenario of the UAV, such as the 50 Hz power frequency interference commonly seen in power line inspection scenarios and specific high-frequency interference generated by equipment operation in industrial monitoring scenarios; then, correlating and comparing the variation patterns of the analyzed electromagnetic field strength data with the real-time waveform of the airborne circuit signal, capturing the signal fluctuations in the airborne circuit signal that occur synchronously with the abnormal changes in electromagnetic field strength, summarizing the frequency range, peak amplitude, duration, and occurrence patterns of these fluctuations, and finally obtaining interference characteristics that can accurately reflect the impact of the airborne circuit signal on the current environment.

[0088] Step 2.2: Based on the interference characteristics, wavelet basis functions and threshold functions are used to perform wavelet decomposition, thresholding, and wavelet reconstruction on the airborne circuit signal to obtain the filtered circuit signal. Specifically, this includes: selecting appropriate wavelet basis functions and threshold functions based on the interference characteristics; if the interference characteristics are mainly low-frequency interference, such as 50 Hz power frequency interference in power inspection scenarios, then the db4 wavelet basis function is selected; if the interference characteristics are mainly high-frequency interference, then the sym5 wavelet basis function is selected; after completing the selection of wavelet basis functions and threshold functions, multi-scale wavelet decomposition is performed on the airborne circuit signal to decompose the circuit signal into approximation coefficients and detail coefficients of different frequency bands, where the approximation coefficients correspond to the useful low-frequency components in the circuit signal, and the detail coefficients correspond to the high-frequency components containing interference; then, according to the frequency range corresponding to the interference characteristics, the selected Sure threshold function is used to perform thresholding on the decomposed detail coefficients to suppress or set the detail coefficients of the corresponding interference frequency bands to zero, while retaining the detail coefficients and approximation coefficients of the corresponding useful signal frequency bands; finally, the processed approximation coefficients and detail coefficients are reconstructed using wavelets to recover the filtered circuit signal with interference components removed.

[0089] In this embodiment of the invention, because the interference characteristics of airborne circuit signals are extracted based on electromagnetic field strength data, the problem of ambiguity in the identification of interference types and characteristics is overcome, thereby accurately locating the frequency range, amplitude characteristics, and duration of the interference. Because wavelet basis functions and threshold functions are selected specifically according to the interference characteristics for wavelet decomposition, threshold processing, and reconstruction, the problem that static filtering algorithms cannot adapt to dynamic electromagnetic interference is overcome, thereby effectively separating and removing interference components, obtaining a pure filtered circuit signal, and improving the signal-to-noise ratio and stability.

[0090] In a preferred embodiment of the present invention, step 3 above may include:

[0091] Step 3.1: Obtain the distribution location information of the multimodal sensors in the three-dimensional space of the UAV. Specifically, this includes: considering the need for the UAV's electronic circuits to accurately grasp the spatial distribution of environmental parameters in complex environments such as strong electric fields, strong magnetic fields, and high temperatures, first determine the three-dimensional coordinate system of the UAV fuselage to establish a unified spatial reference. Then, based on the monitoring function of the multimodal sensors and the layout characteristics of the UAV's electronic circuits, deploy the electromagnetic sensors and temperature sensors around the electronic circuit cabins and near the core circuit components in different directions on the outside of the UAV fuselage. Subsequently, assign a unique three-dimensional spatial coordinate to each deployed multimodal sensor. This coordinate accurately corresponds to the specific position of the sensor in the three-dimensional coordinate system of the UAV fuselage. Finally, obtain the complete distribution location information of the multimodal sensors in the three-dimensional space of the UAV, i.e., the three-dimensional spatial distribution location information of the multimodal sensors.

[0092] Step 3.2 involves fusing the distribution location information with the electromagnetic field strength and temperature data at the corresponding locations to obtain the fused data. Specifically, this includes: based on the three-dimensional spatial distribution location information of the multi-modal sensors, firstly retrieving the electromagnetic field strength and temperature data synchronously collected by each sensor at the corresponding location. These data all have a unified timestamp to ensure spatiotemporal consistency. Then, through data association, the three-dimensional spatial coordinates of each sensor are bound one-to-one with the electromagnetic field strength and temperature data collected by that sensor at the same timestamp to obtain a three-dimensional data set of location electromagnetic field and temperature. Next, all three-dimensional data sets are standardized to ensure that the data collected by different types of sensors are consistent in data format and units, ultimately obtaining the fused data.

[0093] Step 3.3: Based on the fused data, spatial environmental correlation data is obtained to characterize the distribution of environmental parameters in three-dimensional space. Specifically, based on the fused data, the fuselage and surrounding space are first divided into several continuous micro-spatial units according to the UAV's three-dimensional spatial coordinate system. Then, the mean values ​​of electromagnetic field intensity and temperature data in all three-dimensional data sets within each micro-spatial unit are statistically analyzed. At the same time, the changing trend of data within each spatial unit is recorded. Subsequently, a spatial data matrix is ​​constructed based on the statistical results, with three-dimensional spatial coordinates as the index and electromagnetic field intensity and temperature as parameters. This matrix can intuitively reflect the specific values ​​and distribution differences of environmental parameters at different spatial locations. Finally, the spatial data matrix is ​​transformed into a quantifiable distribution chart through data visualization processing to obtain spatial environmental correlation data to characterize the distribution of environmental parameters in three-dimensional space.

[0094] In this embodiment of the invention, because the three-dimensional spatial distribution location information of the multimodal sensors is obtained, the problem of lack of spatial coordinate reference for environmental data is overcome, thereby providing a precise location benchmark for data fusion; because the location information is fused with the corresponding electromagnetic field strength and temperature data, the problem of separation between environmental parameters and spatial location is overcome, thereby establishing a spatial correspondence between data; because spatial environmental correlation data is generated based on the fused data, the problem of unclear three-dimensional distribution characteristics of environmental parameters is overcome, thereby clearly characterizing the distribution pattern of environmental parameters in space and providing complete spatial dimension information for subsequent interference assessment.

[0095] In a preferred embodiment of the present invention, step 4 above may include:

[0096] Step 4.1: Based on the spatial environment correlation data, extract the distribution characteristics of electromagnetic field and temperature in three-dimensional space. The distribution characteristics are composed of multiple sensor data points. Specifically, based on the spatial environment correlation data, which already contains the correspondence between electromagnetic field intensity, temperature and three-dimensional spatial coordinates, first divide the three-dimensional space around the UAV into several continuous sub-regions according to the preset spatial grid division rules, then count the maximum, minimum, and average values ​​of electromagnetic field intensity and the corresponding statistical values ​​of temperature in each sub-region, and simultaneously record the distribution density and numerical change gradient of sensor data points in each sub-region. By analyzing these statistical results, identify areas with abnormally high electromagnetic field intensity and abnormally high temperature, classify the sensor data points in these areas according to spatial location and numerical characteristics, and finally extract the distribution characteristics of electromagnetic field and temperature in three-dimensional space composed of multiple sensor data points. These characteristics intuitively reflect the electromagnetic and temperature interference state at different spatial locations.

[0097] Step 4.2: Based on the filtered circuit signal, select key data points as vertices of the pyramid from multiple sensor data points included in the distribution features. Specifically, this includes: calling the filtered circuit signal, which has removed some interference and can more accurately reflect the actual working state of the circuit; firstly analyzing the signal-to-noise ratio fluctuation and signal distortion of the filtered circuit signal to determine the time period and frequency range where the signal is most significantly interfered with; then, combining multiple sensor data points in the distribution features, correlating and comparing the electromagnetic field strength and temperature values ​​corresponding to the data points with the degree of signal interference, and selecting the data points with the highest electromagnetic field strength or temperature and the most significant impact on the signal during the period when the signal is most significantly interfered with. These data points are used as key data points that can represent the core interference area and are marked as vertices of the pyramid.

[0098] Step 4.3: Construct an irregular pyramid based on the vertices of the pyramid, and characterize the spatiotemporal distribution intensity of electromagnetic interference by calculating the geometric volume of the irregular pyramid. Specifically, this includes: First, spatially locating each vertex according to its three-dimensional spatial coordinates based on the vertices. Then, introducing the time dimension based on the timestamp information corresponding to the vertices, taking vertices within the same time period as the same group of interference feature vertices, taking the center point of the core area of ​​the UAV electronic circuit as the common vertex of the pyramid, and taking the key data points as the base vertices of the pyramid. Connecting the common vertex with each base vertices forms an irregular pyramid. The spatial range of this pyramid covers the main interference area. Then, by calculating the geometric volume of this irregular pyramid, the distribution range of electromagnetic interference in three-dimensional space and the continuous influence in the time dimension are comprehensively characterized. The larger the volume, the wider the spatial distribution of the interference or the longer the time influence, thus obtaining the spatiotemporal distribution intensity of electromagnetic interference.

[0099] Step 4.4: Based on the spatiotemporal distribution intensity, obtain the dynamic interference weights used to quantify the degree of interference. Specifically, this includes: first, setting a baseline threshold for the interference intensity based on the spatiotemporal distribution intensity of the electromagnetic interference. This threshold is determined based on the anti-interference capability of the UAV's electronic circuitry and the interference intensity range during normal operation. Then, comparing the calculated spatiotemporal distribution intensity with the baseline threshold to obtain the ratio, this ratio is used as the base weight value. Simultaneously, considering the current stability coefficient of the filtered circuit signal, if the signal stability is poor, the base weight value is appropriately increased; if the signal stability is good, the base weight value is appropriately decreased. Finally, a dynamic interference weight that can reflect the current interference's impact on the circuit in real time is obtained.

[0100] In this embodiment of the invention, the three-dimensional distribution characteristics of electromagnetic field and temperature are extracted based on spatial environment correlation data, overcoming the problem of fragmented spatial distribution characteristics of environmental parameters and obtaining a structured distribution basis composed of data points from multiple sensors. Key data points are selected as vertices of a pyramid based on filtered circuit signals, thus overcoming the problem of data point redundancy or insufficient representativeness, thereby determining the vertices that reflect the core interference and laying the foundation for quantitative calculation. Because an irregular pyramid is constructed and its volume characterizes the spatiotemporal distribution intensity of electromagnetic interference, the problem of difficulty in spatiotemporally quantifying interference intensity is overcome, thus intuitively presenting the distribution range and strength of interference in three-dimensional space. Because dynamic interference weights are obtained based on spatiotemporal distribution intensity, the problem of lacking precise quantitative indicators for interference degree is overcome, thus providing a directly applicable quantitative basis for the dynamic adjustment of subsequent protection strategies.

[0101] In a preferred embodiment of the present invention, step 5 above may include:

[0102] Step 5.1: Based on the dynamic interference weight, obtain the inverse compensation signal that is opposite in phase and corresponds to the amplitude of the low-frequency electromagnetic interference. Specifically, this includes: based on the dynamic interference weight, which quantifies the influence of the current low-frequency electromagnetic interference, firstly, extract the phase characteristics and amplitude variation law of the residual low-frequency electromagnetic interference in the filtered circuit signal, and then determine the amplitude ratio of the inverse compensation signal according to the value of the dynamic interference weight. The larger the weight, the more the compensation amplitude increases proportionally to match the stronger interference. At the same time, generate a signal waveform that is completely opposite in phase to the extracted low-frequency interference, and finally obtain the inverse compensation signal that is opposite in phase and whose amplitude corresponds to the interference intensity. The parameter settings of this signal are directly related to the quantization result of the dynamic interference weight.

[0103] Step 5.2 involves real-time superposition of the inverse compensation signal and the filtered circuit signal to obtain the compensated circuit signal. Specifically, this includes: calling the inverse compensation signal and the filtered circuit signal, connecting the two signals to the superposition circuit under the same time reference, ensuring precise alignment of the inverse compensation signal and the filtered circuit signal on the time axis through a synchronous triggering mechanism, and then synthesizing the two signals in real time through signal superposition operations. The low-frequency components of the inverse compensation signal cancel out the low-frequency interference components remaining in the filtered circuit signal, ultimately obtaining the compensated circuit signal.

[0104] Step 5.3: Based on the compensation of the circuit signal to cancel the low-frequency electromagnetic interference component, obtain the circuit signal state after anti-interference preprocessing. Specifically, this includes: based on the compensation of the circuit signal, detecting the residual intensity of the low-frequency component in real time, comparing the amplitude change of the low-frequency interference before and after compensation, confirming that the reverse compensation signal and the residual low-frequency electromagnetic interference component effectively cancel each other out after superposition, and when the low-frequency interference amplitude is detected to drop to the threshold range allowed for normal circuit operation, recording the waveform characteristics, signal-to-noise ratio and stability parameters of the circuit signal at this time, and integrating these parameters into the circuit signal state after anti-interference preprocessing.

[0105] In this embodiment of the invention, an inverse compensation signal with the opposite phase and corresponding amplitude to the low-frequency electromagnetic interference is obtained according to the dynamic interference weight. This overcomes the problem of mismatch between the compensation signal and the actual low-frequency interference parameters, thus allowing the compensation signal to accurately adapt to the current interference intensity. Because the inverse compensation signal is superimposed on the filtered circuit signal in real time, the problem of static compensation being unable to keep up with the dynamic changes in interference is overcome, thereby achieving real-time targeted cancellation of low-frequency interference. Because the low-frequency electromagnetic interference component is canceled based on the compensated circuit signal, the problem of low-frequency interference residue affecting the stability of the circuit signal is overcome, thereby obtaining the circuit signal state after anti-interference preprocessing.

[0106] In a preferred embodiment of the present invention, step 6 above may include:

[0107] Step 6.1: Match the dynamic interference weights with the preset interference heat load mapping table to obtain the corresponding basic heat load coefficient. Specifically, this includes: based on the dynamic interference weights, calling the preset interference heat load mapping table, which was generated experimentally under complex scenarios such as strong electric and magnetic fields and high temperatures faced by UAV electronic circuits. The table pre-stores the correlation between different dynamic interference weight ranges and corresponding basic heat load coefficients. The current dynamic interference weights are compared with the ranges in the mapping table, and after finding the matching interval, the corresponding basic heat load coefficient is extracted. This coefficient initially reflects the basic heating level of the circuit under the current interference intensity.

[0108] Step 6.2 involves dynamically correcting the basic heat load coefficient and obtaining the real-time heat load index based on the signal amplitude change rate in the circuit signal state after anti-interference preprocessing. Specifically, this includes: based on the basic heat load coefficient, and considering the problem of static evaluation of heat load being prone to lag in the background technology, retrieving the circuit signal state after anti-interference preprocessing, extracting the signal amplitude change rate from it to reflect the real-time load fluctuation of the circuit, adjusting the correction range of the basic heat load coefficient according to the magnitude of the signal amplitude change rate (the more severe the load fluctuation, the more the correction range is adjusted), and then adjusting the basic heat load coefficient according to the correlation law between circuit load fluctuation and heat load. Finally, the corrected coefficient and the correlation parameter of the signal amplitude change rate are integrated to obtain the real-time heat load index that can reflect the heating trend of the circuit in real time.

[0109] Step 6.3 involves weighted fusion of the real-time heat load index and temperature data to obtain a comprehensive heat load assessment value. Specifically, this includes: obtaining a comprehensive heat load assessment value based on the real-time heat load index and temperature data collected by multi-modal sensors using a weighted fusion method; determining the weight ratio of the two based on the current environmental characteristics; increasing the weight ratio of temperature data if the temperature data exceeds the normal operating temperature threshold of the circuit; increasing the weight ratio of the index if the dynamic interference weight is high, resulting in a larger real-time heat load index; and then weighting the real-time heat load index and the temperature data after conversion to standardized heat load values ​​according to the determined weight ratio, and then adding them together to obtain the comprehensive heat load assessment value.

[0110] Step 6.4: Based on the preset threshold range to which the comprehensive heat load assessment value belongs, obtain a quantitative temperature control command containing frequency adjustment parameters and power adjustment parameters. Specifically, this includes: based on the comprehensive heat load assessment value, calling the preset heat load threshold range table. This range table is set according to the temperature resistance capability and normal operating power range of the UAV's onboard circuit, and contains multiple preset heat load threshold ranges. Each range corresponds to preset frequency adjustment parameters and power adjustment parameters. Compare the current comprehensive heat load assessment value with the threshold range in the range table, determine the range to which it belongs, extract the corresponding frequency adjustment parameters and power adjustment parameters, and integrate them to form a quantitative temperature control command containing specific values.

[0111] Step 6.5: Based on the quantized temperature control command, synchronously reduce the operating frequency and power supply of the onboard circuit to achieve heat dissipation balance. Specifically, this includes: sending the frequency adjustment parameters in the quantized temperature control command to the clock control area of ​​the circuit, gradually reducing the operating frequency of the circuit according to the parameter requirements, and simultaneously sending the power adjustment parameters to the power supply management area, synchronously reducing the power supply according to the parameters. During the adjustment process, the heat dissipation of the circuit is monitored in real time by a temperature sensor to confirm whether the temperature gradually drops, ensuring that the adjustment range of frequency and power can balance the heat generation and heat dissipation of the circuit, avoiding heat dissipation imbalance caused by adjusting only the frequency or power, and ultimately achieving thermal stability of the circuit in complex environments.

[0112] In this embodiment of the invention, the basic heat load coefficient is obtained by matching the dynamic interference weight with the interference heat load mapping table, thus overcoming the problem of ambiguity in the correlation between interference and heat load, and establishing a basic quantitative relationship between interference and circuit heating. Because the basic heat load coefficient is dynamically corrected and combined with the signal amplitude change rate to obtain the real-time heat load index, the problem of static thermal assessment lagging behind circuit state changes is overcome, thus accurately reflecting the current real-time heating trend of the circuit. Because the real-time heat load index and temperature data are weighted and fused to obtain a comprehensive heat load assessment value, the problem of the one-sidedness of single-parameter assessment of thermal state is overcome, thus comprehensively reflecting the true level of circuit heat load. Because a quantitative temperature control command is obtained based on the threshold range to which the comprehensive assessment value belongs, the problem of temperature control command lacking precise parameters is overcome, thus providing clear frequency and power parameters for circuit adjustment. Because the circuit operating frequency and power supply are adjusted synchronously according to the command, the problem of heat dissipation and power consumption imbalance is overcome, thus achieving heat dissipation balance and ensuring stable operation of the circuit in complex environments.

[0113] In a preferred embodiment of the present invention, step 7 above may include:

[0114] Step 7.1: During the dynamic adjustment process, vibration data is continuously collected using an inertial sensor, and the time-domain and frequency-domain features of the vibration data are extracted. Specifically, during the dynamic adjustment of the airborne circuit operating frequency and power supply, vibration data is continuously collected using an inertial sensor at a preset sampling interval. The sampling interval is set according to the amplitude of the dynamic adjustment; the larger the adjustment amplitude, the smaller the sampling interval to capture instantaneous vibration changes. At the same time, features are extracted from the collected vibration data. The time-domain features include the maximum, minimum, and average amplitude of the vibration and the vibration period. The frequency-domain features include the main frequency components of the vibration signal, the frequency peak value, and the frequency distribution range. These features can comprehensively reflect the intensity and pattern of the vibration.

[0115] Step 7.2 involves displaying and analyzing the time-domain and frequency-domain characteristics of the vibration data. When the characteristic value exceeds the preset safety threshold, it is determined to be an abnormal vibration signal. Specifically, this includes: based on the time-domain and frequency-domain characteristics of the vibration data, firstly, calling the historical database of vibration characteristics under normal operating conditions of the UAV, which contains the vibration characteristic range of the circuit under different operating conditions, and then comparing and analyzing the real-time extracted time-domain and frequency-domain characteristic values ​​with the normal range in the database. At the same time, combined with the preset safety threshold, which is set according to the vibration resistance limit of the precision components of the circuit and the vibration critical value that does not affect signal transmission, when the real-time characteristic value (such as the maximum amplitude or a specific frequency peak) exceeds the preset safety threshold, it is determined that there is an abnormal vibration signal that may interfere with the circuit signal or damage the components.

[0116] Step 7.3: Obtain a trigger command based on the abnormal vibration signal and simultaneously activate the signal protection mechanism. Specifically, this includes: generating a trigger command based on the abnormal vibration signal, which includes the type of abnormal vibration (such as low-frequency mechanical vibration or high-frequency electromagnetic vibration), the location of occurrence, and the intensity level. The level of the trigger command is positively correlated with the amplitude of the characteristic value exceeding the threshold. The larger the amplitude, the higher the command level. At the same time, activate the pre-configured signal protection mechanism, which includes measures such as temporarily stabilizing the signal transmission link and enhancing the signal driving capability, to avoid the abnormal vibration directly causing circuit signal interruption or distortion.

[0117] Step 7.4: Based on the trigger instruction, reallocate power supply resources and reduce the power supply priority of non-critical circuits. Specifically, based on the trigger instruction, reallocate computing resources through preset process scheduling logic. First, identify the processing threads in the system that correspond to the functions of non-critical circuits. These threads are responsible for auxiliary signal processing or non-core data operations. Then, reduce their priority in the system scheduling queue and reduce the frequency at which they acquire CPU time slices. At the same time, allocate the released computing resources to the core processing threads corresponding to critical circuits.

[0118] Step 7.5: Based on the reduction in power supply priority, activate the pre-configured backup signal path. Specifically, based on the reduction in priority of non-critical circuit processing threads, the core processing threads of critical circuits have obtained sufficient computing resources. At this time, the preset software routing configuration logic is invoked. This logic pre-stores the mapping relationship between the backup signal path and the main signal path, including the call path of the signal processing function, the address mapping of the data buffer, and other information. By modifying the path pointing parameters in the signal processing flow, the signal data flow of the critical circuit is switched from the processing link of the main signal path to the processing link of the backup signal path, thus completing the activation of the backup signal path and ensuring that signal transmission is not interrupted by interference that may be affected by vibration in the main path.

[0119] Step 7.6: Monitor and record the operating status of the backup signal path in real time. The operating status includes signal transmission quality and stability parameters. Specifically, after activating the backup signal path, a preset status monitoring thread is started. This thread extracts operating parameters from the processing link of the backup signal path at a fixed period. The signal transmission quality parameters include the transmission delay of data frames, the number of verification failures, and the proportion of valid data. The stability parameters include the duration of continuous normal transmission and the maximum amplitude of parameter fluctuations. The monitoring thread writes these parameters into a preset log data structure in the order of collection time to form the operating status record of the backup signal path.

[0120] In this embodiment of the invention, vibration data is continuously collected and time-domain and frequency-domain features are extracted by inertial sensors during dynamic adjustment, thus overcoming the problems of incomplete vibration data collection and single feature dimensions, and providing a complete data foundation for abnormal vibration identification. Because the time-domain and frequency-domain features of the vibration data are analyzed and an abnormal vibration signal is determined when the feature value exceeds a preset safety threshold, the problem of difficulty in accurately identifying abnormal vibration is overcome, thus timely capturing vibration risks during circuit operation. Because a trigger command is obtained based on the abnormal vibration signal and a signal protection mechanism is activated, the problem of lack of timely protection response after an anomaly occurs is overcome, thus quickly entering a targeted protection state. Because power supply resources are reallocated and the power supply priority of non-critical circuits is reduced according to the trigger command, the problem of unbalanced power supply resource allocation is overcome, thus ensuring stable power supply to critical circuits. Because a pre-configured backup signal path is activated based on power supply priority adjustment, the problem of signal transmission interruption after a main signal path anomaly is overcome, thus maintaining continuous circuit signal transmission. Because the signal transmission quality and stability parameters of the backup signal path are monitored and recorded in real time, the problem of unclear backup path operating status is overcome.

[0121] In a preferred embodiment of the present invention, step 8 above may include:

[0122] Step 8.1: Extract the signal transmission quality parameters and stability parameters of the backup signal path. Specifically, this includes: based on the operating status of the backup signal path, extracting the signal transmission quality parameters and stability parameters from the stored operating logs. The signal transmission quality parameters include data frame transmission delay, number of verification failures, and percentage of valid data. The stability parameters include continuous normal transmission duration and maximum parameter fluctuation. During extraction, the parameters closest to the current time are selected by timestamp to ensure that the extracted parameters reflect the latest operating status of the backup signal path.

[0123] Step 8.2 involves fusing and analyzing the signal transmission quality parameters, stability parameters, and electromagnetic field strength data from the current monitoring data to assess the overall quality level of the current signal transmission environment. This includes: retrieving the signal transmission quality parameters and stability parameters, and simultaneously retrieving the electromagnetic field strength data from the current monitoring data. The three types of data are incorporated into a unified analysis framework. First, the parameters are standardized to eliminate dimensional differences. Then, a mapping relationship between the path operation parameters and the electromagnetic field strength is established through correlation analysis. For example, the trend of signal transmission delay changes when the electromagnetic field strength increases. Based on the mapping relationship, the impact of the current signal transmission environment on the path operation is comprehensively assessed, and the impact is divided into three comprehensive quality levels: excellent, medium, and poor, according to preset standards.

[0124] Step 8.3 involves weighting and correcting the overall quality level, and obtaining the environmental adaptability assessment result based on the dynamic interference weight. Specifically, this includes: weighting and correcting the overall quality level in combination with the dynamic interference weight. If the dynamic interference weight is greater than a preset threshold, it indicates that electromagnetic interference has a significant impact on signal transmission. In this case, the weight of electromagnetic field strength data in the correction is increased. If the overall quality level is poor but the dynamic interference weight is low, the correction magnitude is appropriately reduced to avoid over-evaluation. The overall quality level is converted into a quantified environmental adaptability value through correction. The higher the value, the more suitable the current environment is for signal transmission. Finally, an environmental adaptability assessment result that accurately reflects the degree of adaptation between the environment and the pathway is obtained.

[0125] Step 8.4: Based on the environmental fitness assessment results, adaptively select the corresponding signal modulation mode parameters, coding rate parameters, and retransmission mechanism parameters from the preset strategy library. Specifically, this includes: based on the environmental fitness assessment results, calling the preset signal processing strategy library, which pre-stores signal modulation mode parameters (such as amplitude modulation, frequency modulation, phase modulation, and specific modulation depth), coding rate parameters (such as symbol transmission rate under different coding modes), and retransmission mechanism parameters (such as retransmission count and retransmission interval) corresponding to different environmental fitness ranges. For example, when the environmental fitness is low, it corresponds to a modulation mode with strong anti-interference ability, a lower coding rate, and a higher number of retransmissions. Match the current environmental fitness assessment results with the ranges in the strategy library, extract the corresponding three types of parameters, and ensure that the selected parameters can adapt to the overall quality of the current signal transmission environment.

[0126] Step 8.5 integrates the signal modulation parameters, coding rate parameters, and retransmission mechanism parameters to obtain the adjusted signal processing strategy. Specifically, this includes: integrating the signal modulation parameters, coding rate parameters, and retransmission mechanism parameters according to the signal processing flow; first, determining the coordination relationship between parameters, such as the modulation method determining the selectable range of coding rate, and the retransmission mechanism needing to match the coding rate to avoid transmission conflicts; then, embedding the parameters into the corresponding processing stages in the order of signal transmission, encoding, transmission, and retransmission to form a complete signal processing flow specification. This specification clarifies the specific operating parameters of each stage, ultimately yielding the adjusted signal processing strategy.

[0127] In this embodiment of the invention, because the signal transmission quality parameters and stability parameters of the backup signal path are extracted, the problem of lacking actual operational data support for subsequent evaluation is overcome; because the signal transmission quality parameters, stability parameters, and current electromagnetic field strength data are fused and analyzed to evaluate the comprehensive quality level, the problem of one-sided evaluation of environmental quality by a single parameter is overcome; because the comprehensive quality level is weighted and corrected and combined with dynamic interference weights to obtain the environmental adaptability evaluation result, the problem of the evaluation result being out of sync with the actual interference situation is overcome; because the signal modulation, coding rate, and retransmission mechanism parameters are adaptively selected from the preset strategy library based on the environmental adaptability evaluation result, the problem of blind selection of signal processing parameters and inability to match environmental changes is overcome, thereby ensuring that the parameters are adapted to the current environmental requirements; because the selected parameters are integrated to obtain the adjusted signal processing strategy, the problem of scattered parameters and difficulty in direct application is overcome, thereby forming a complete and usable strategy.

[0128] In a preferred embodiment of the present invention, step 9 above may include:

[0129] Step 9.1: Obtain a mixed data stream containing multiple types of data output by the UAV. Specifically, this includes: collecting various input and output data about the UAV based on its real-time operating status, including flight control commands, navigation and positioning information, equipment status feedback, environmental monitoring data, etc., and summarizing and integrating these data from different sources and in different formats to form a mixed data stream containing multiple types of data.

[0130] Step 9.2: Based on the signal modulation method parameters and coding rate parameters, the mixed data stream is parsed and its features are identified to obtain the identified data features. Specifically, this includes: calling the signal modulation method parameters and coding rate parameters, parsing the mixed data stream based on these parameters, restoring the information carried by the data according to the signal demodulation rules corresponding to the modulation method, parsing the structure and content of the data according to the decoding standard corresponding to the coding rate, and extracting feature information such as type identifier, transmission timestamp, and data check code from the data. The category attributes and format features of different data are identified through feature matching, and finally, the identified data features are obtained.

[0131] Step 9.3: Based on the identified data features, extract flight control data and navigation and positioning data from the mixed data stream as key data streams. Specifically, based on the identified data features and according to the core requirements for safe flight of UAVs, filter out data related to flight attitude control, route adjustment, and real-time positioning, and extract these data that directly affect the flight safety and trajectory accuracy of UAVs from the mixed data stream, and determine them as key data streams that need to be prioritized for protection.

[0132] Step 9.4: Transmit the critical data stream through a dedicated physical channel and encapsulate the data to achieve physical isolation from other data streams, resulting in an isolated critical data stream. Specifically, this includes: enabling a pre-defined dedicated physical channel in the system for the critical data stream. This channel is hardware-independent from the channel transmitting non-critical data to avoid signal crosstalk. At the same time, the critical data stream is encapsulated in a dedicated format, and a dedicated isolation identifier and encryption verification field are added to the data frame header. This allows the encapsulated critical data stream to be identified as an independent transmission unit during transmission, achieving complete physical isolation from other data streams and preventing non-critical data from interfering with it.

[0133] Step 9.5 involves calculating the real-time priority coefficients for the isolated critical data streams. Based on the dynamic interference weight and environmental adaptability assessment results, the calculated real-time priority coefficients are obtained. Specifically, this includes: for the isolated critical data streams, combining the dynamic interference weight and environmental adaptability assessment results, firstly, setting basic priority coefficients for flight control data and navigation and positioning data respectively. The basic coefficients are pre-determined based on the inherent importance of the data to the safe flight of the UAV, with the basic coefficient for flight control data being higher than that for navigation and positioning data. Next, the priority coefficient for flight control data is adjusted according to the magnitude of the dynamic interference weight. When the dynamic interference weight is greater than the upper limit of the preset interval, it indicates that the current electromagnetic environment is severely interfering with the circuit signal. In this case, the priority coefficient for flight control data is increased by a preset ratio, with a smaller increase for a smaller weight. Simultaneously, the priority coefficient for navigation and positioning data is adjusted according to the environmental adaptability assessment results. When the environmental adaptability assessment results are lower than the lower limit of the preset interval, it indicates that the signal transmission environment is poor and navigation and positioning data is easily affected. In this case, the priority coefficient for navigation and positioning data is increased by a corresponding ratio, with a smaller increase for a higher adaptability. Through the above adjustments, the real-time priority coefficients for various critical data streams in the current environment are finally obtained. The higher the coefficient value, the stronger the urgency and importance of data transmission.

[0134] Step 9.6: Based on the calculated real-time priority coefficients, allocate corresponding bandwidth resources and transmission time slots to different categories of isolated critical data streams to obtain the final scheduling instruction, thereby completing the isolation and priority scheduling of critical data streams. Specifically, this includes: allocating resources to different categories of isolated critical data streams according to the real-time priority coefficients; allocating the highest priority critical data stream with the widest bandwidth resources and the earliest transmission time slot, and allocating the next highest priority critical data stream with correspondingly reduced bandwidth and slightly later time slots, and so on, from highest to lowest coefficient. The allocation results are then integrated into a final scheduling instruction containing bandwidth values, time slot periods, and transmission order. The instruction controls the priority scheduling of data transmission to ensure that critical data streams are transmitted in an orderly manner according to priority.

[0135] In this embodiment of the invention, by acquiring the mixed data stream of the UAV, the problem of lacking original data in subsequent processing is overcome, providing a complete source for analysis and key data extraction; by analyzing the mixed data stream and identifying features based on signal parameters, the problems of inaccurate analysis and feature deviation are overcome, resulting in accurate data features; by extracting flight control and navigation positioning data based on features as the key data stream, the problem of key data being overwhelmed is overcome, focusing on core data; by transmitting and encapsulating the key data stream through a dedicated physical channel to achieve physical isolation, the problem of key data being interfered with is overcome, ensuring safe and independent transmission; by combining dynamic interference weights and environmental adaptability to calculate the real-time priority coefficient of the key data stream, the problem of fixed priority is overcome, adapting to the current environment and transmission status; by allocating bandwidth and transmission time slots according to the real-time priority coefficient, the problem of unreasonable allocation of core data resources is overcome, completing the isolation and priority scheduling of the key data stream, ensuring stable and efficient transmission of core data.

[0136] like Figure 2 As shown, embodiments of the present invention also provide an electronic circuit protection system for unmanned aerial vehicles (UAVs), comprising:

[0137] The acquisition module is used to collect electromagnetic field strength data and temperature data of the environment in which the UAV is located in real time through multimodal sensors;

[0138] The calculation module uses wavelet threshold noise reduction algorithm to filter the airborne circuit signals based on electromagnetic field strength data to obtain filtered circuit signals; it also fuses the distribution of multimodal sensors in three-dimensional space with electromagnetic field strength data and temperature data to obtain space environment correlation data.

[0139] The module constructs dynamic interference weights by using a pyramidal volume algorithm to calculate the spatiotemporal distribution intensity of current electromagnetic interference based on spatial environment correlation data and filtered circuit signals.

[0140] The processing module performs real-time compensation processing on low-frequency electromagnetic interference based on dynamic interference weights to obtain the circuit signal state after anti-interference preprocessing.

[0141] The adjustment module, based on the circuit signal state after anti-interference preprocessing, temperature data, and dynamic interference weights, obtains temperature control commands and dynamically adjusts the circuit operating frequency and power according to the temperature control commands. During the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected by the inertial sensor, a signal protection mechanism is triggered, reducing the power supply priority of non-critical circuits. Then, a backup signal path is started, and the operating status of the backup signal path is recorded. Based on the operating status of the backup signal path, current monitoring data, and dynamic interference weights, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy.

[0142] The scheduling module, based on the adjusted signal processing strategy, obtains the current critical data stream and isolates and prioritizes the critical data stream.

[0143] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for protecting the electronic circuits of a drone, characterized in that, The method includes: The electromagnetic field strength and temperature data of the environment in which the drone is located are collected in real time by multimodal sensors. Based on electromagnetic field strength data, a wavelet threshold noise reduction algorithm is used to filter the airborne circuit signal to obtain the filtered circuit signal. By fusing the distribution of multimodal sensors in three-dimensional space with electromagnetic field intensity data and temperature data, spatial environment correlation data can be obtained. Based on spatial environment correlation data and filtered circuit signals, the pyramidal volume algorithm is used to calculate the spatiotemporal distribution intensity of current electromagnetic interference in order to construct dynamic interference weights. Based on the dynamic interference weight, low-frequency electromagnetic interference is compensated in real time to obtain the circuit signal state after anti-interference preprocessing. Based on the circuit signal state, temperature data, and dynamic interference weights after anti-interference preprocessing, temperature control commands are obtained, and the circuit operating frequency and power are dynamically adjusted according to the temperature control commands. During the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected based on the inertial sensor, a signal protection mechanism is triggered to reduce the power supply priority of non-critical circuits. Then, the backup signal path is started and the operating status of the backup signal path is recorded. Based on the operating status of the backup signal path, current monitoring data, and dynamic interference weights, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy. Based on the adjusted signal processing strategy, the current critical data stream is obtained, and the critical data stream is isolated and prioritized.

2. The method for protecting the electronic circuits of a drone according to claim 1, characterized in that, Based on electromagnetic field strength data, a wavelet threshold noise reduction algorithm is used to filter the airborne circuit signals, resulting in the filtered circuit signals, including: Based on electromagnetic field strength data, interference characteristics in airborne circuit signals are obtained; Based on the interference characteristics, wavelet basis functions and threshold functions are used to perform wavelet decomposition, thresholding, and wavelet reconstruction on the airborne circuit signal to obtain the filtered circuit signal.

3. The method for protecting the electronic circuits of a drone according to claim 2, characterized in that, By fusing the distribution locations of multimodal sensors in three-dimensional space with electromagnetic field intensity data and temperature data, spatial environment-related data are obtained, including: Acquire the distribution and location information of multimodal sensors in the three-dimensional space of the UAV; The distribution location information is fused with the electromagnetic field strength data and temperature data at the corresponding locations to obtain the fused data; Based on the fused data, spatial environmental association data is obtained to characterize the distribution of environmental parameters in three-dimensional space.

4. The method for protecting the electronic circuits of a drone according to claim 3, characterized in that, Based on spatial environment correlation data and filtered circuit signals, the pyramidal volume algorithm is used to calculate the spatiotemporal distribution intensity of current electromagnetic interference in order to construct dynamic interference weights, including: Based on spatial environment correlation data, the distribution characteristics of electromagnetic field and temperature in three-dimensional space are extracted, where the distribution characteristics are composed of multiple sensor data points; Based on the filtered circuit signal, key data points are selected as the vertices of the pyramid from multiple sensor data points contained in the distribution characteristics. An irregular pyramid is constructed based on the vertices of the pyramid, and the spatiotemporal distribution intensity of electromagnetic environmental interference is characterized by calculating the geometric volume of the irregular pyramid. Based on the spatiotemporal distribution intensity, a dynamic interference weight is obtained to quantify the degree of interference.

5. A method for protecting the electronic circuits of a drone according to claim 4, characterized in that, Based on dynamic interference weights, low-frequency electromagnetic interference is compensated in real time to obtain the circuit signal state after anti-interference preprocessing, including: Based on the dynamic interference weight, an inverse compensation signal with the opposite phase and corresponding amplitude to the low-frequency electromagnetic interference is obtained; The inverse compensation signal is superimposed on the filtered circuit signal in real time to obtain the compensated circuit signal. Based on the compensation of the circuit signal to cancel out the low-frequency electromagnetic interference components, the circuit signal state after anti-interference preprocessing is obtained.

6. A method for protecting the electronic circuits of a drone according to claim 5, characterized in that, Based on the circuit signal state, temperature data, and dynamic interference weights after anti-interference preprocessing, a temperature control command is obtained. The circuit operating frequency and power are then dynamically adjusted according to this command, including: The dynamic disturbance weights are matched with the preset disturbance heat load mapping table to obtain the corresponding basic heat load coefficients. The basic heat load coefficient is dynamically corrected, and the real-time heat load index is obtained based on the rate of change of signal amplitude in the circuit signal state after anti-interference preprocessing. The real-time heat load index and temperature data are weighted and fused to obtain a comprehensive heat load assessment value; Based on the preset threshold range to which the comprehensive heat load assessment value belongs, a quantitative temperature control command including frequency adjustment parameters and power adjustment parameters is obtained; According to the quantized temperature control command, the operating frequency and power supply of the airborne circuits are reduced synchronously to achieve heat dissipation balance.

7. A method for protecting the electronic circuits of a drone according to claim 6, characterized in that, During the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected based on the inertial sensor, a signal protection mechanism is triggered, reducing the power supply priority of non-critical circuits. Then, the backup signal path is activated, and the operating status of the backup signal path is recorded, including: During the dynamic adjustment process, vibration data is continuously collected by an inertial sensor, and the time-domain and frequency-domain features of the vibration data are extracted simultaneously. The time-domain and frequency-domain characteristics of vibration data are displayed and analyzed. When the characteristic value exceeds the preset safety threshold, it is determined to be an abnormal vibration signal. A trigger command is received based on the abnormal vibration signal, and the signal protection mechanism is activated simultaneously. Based on the trigger command, power supply resources are reallocated, and the power supply priority of non-critical circuits is reduced. Based on the reduction of power supply priority, the pre-configured backup signal path is activated; The operation status of the backup signal path is monitored and recorded in real time, including signal transmission quality and stability parameters.

8. A method for protecting the electronic circuits of a drone according to claim 7, characterized in that, Based on the operating status of the backup signal path, current monitoring data, and dynamic interference weights, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy, including: Extract the signal transmission quality and stability parameters of the backup signal path; By fusing and analyzing signal transmission quality parameters, stability parameters, and electromagnetic field strength data from current monitoring data, the overall quality level of the current signal transmission environment can be assessed. The overall quality level is weighted and corrected, and the environmental adaptability assessment result is obtained based on the dynamic disturbance weight. Based on the environmental adaptability assessment results, the corresponding signal modulation parameters, coding rate parameters, and retransmission mechanism parameters are adaptively selected from the preset strategy library; By integrating the signal modulation parameters, coding rate parameters, and retransmission mechanism parameters, the adjusted signal processing strategy is obtained.

9. A method for protecting the electronic circuits of a drone according to claim 8, characterized in that, Based on the adjusted signal processing strategy, the current critical data stream is obtained, and the critical data stream is isolated and prioritized, including: Acquire a mixed data stream containing multiple data types output by the drone; Based on the signal modulation parameters and coding rate parameters, the mixed data stream is analyzed and its features are identified to obtain the identified data features; Based on the identified data features, flight control data and navigation and positioning data are extracted from the mixed data stream as key data streams; The critical data stream is transmitted through a dedicated physical channel and encapsulated to achieve physical isolation from other data streams, resulting in an isolated critical data stream. Real-time priority coefficients are calculated for isolated critical data streams, and the calculated real-time priority coefficients are obtained based on the dynamic interference weight and environmental adaptability assessment results. Based on the calculated real-time priority coefficients, corresponding bandwidth resources and transmission time slots are allocated to different categories of isolated critical data streams to obtain the final scheduling instructions, thereby completing the isolation and priority scheduling of critical data streams.

10. A protective device for the electronic circuits of a drone, the device implementing the method as described in any one of claims 1 to 9, characterized in that, include: The acquisition module is used to collect electromagnetic field strength data and temperature data of the environment in which the UAV is located in real time through multimodal sensors; The calculation module uses wavelet threshold noise reduction algorithm to filter the airborne circuit signals based on electromagnetic field strength data to obtain filtered circuit signals; it also fuses the distribution of multimodal sensors in three-dimensional space with electromagnetic field strength data and temperature data to obtain space environment correlation data. The module constructs dynamic interference weights by using a pyramidal volume algorithm to calculate the spatiotemporal distribution intensity of current electromagnetic interference based on spatial environment correlation data and filtered circuit signals. The processing module performs real-time compensation processing on low-frequency electromagnetic interference based on dynamic interference weights to obtain the circuit signal state after anti-interference preprocessing. The adjustment module obtains temperature control commands based on the circuit signal state, temperature data, and dynamic interference weights after anti-interference preprocessing, and dynamically adjusts the circuit operating frequency and power according to the temperature control commands. During the dynamic adjustment process, current monitoring data is collected in real time. If an abnormal vibration signal is detected by the inertial sensor, a signal protection mechanism is triggered to reduce the power supply priority of non-critical circuits. Then, the backup signal path is started and the operating status of the backup signal path is recorded. Based on the operating status of the backup signal path, the current monitoring data, and the dynamic interference weight, the signal processing strategy is adaptively adjusted to obtain the adjusted signal processing strategy. The scheduling module, based on the adjusted signal processing strategy, obtains the current critical data stream and isolates and prioritizes the critical data stream.

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