Sensor signal buffering method and system for high impact scenarios

Abstract

The application relates to the technical field of sensor signals, in particular to a sensor signal buffering method and system for a high-impact scene. The method comprises the following steps: obtaining sensor operation data to perform sensor signal state identification and sensor impact identification, and generating sensor signal state data and sensor impact data respectively; performing sensor impact and signal coupling disturbance analysis based on the sensor impact data and the sensor signal state data, and generating sensor impact-signal coupling disturbance data; performing sensor signal multi-channel collaborative buffering processing according to the sensor impact-signal coupling disturbance data, and generating sensor signal multi-channel collaborative buffering data; and performing sensor buffering signal fluctuation and attenuation analysis and sensor signal buffering stability and enhancement processing according to the sensor signal multi-channel collaborative buffering data. The application realizes sensor signal buffering in a high-impact scene.

Description

Technical Field

[0001] This invention relates to the field of sensor signal technology, and in particular to a sensor signal buffering method and system for high-impact scenarios. Background Technology

[0002] With the widespread deployment of sensors in intelligent equipment, industrial inspection, and aerospace systems operating under high-impact and high-vibration environments, ensuring stable acquisition and effective buffering of sensor signals under complex physical disturbances has become crucial for reliable system operation. Current technologies typically employ signal filtering, channel redundancy, and data buffering to address signal interference caused by sensor impacts. However, these methods have significant limitations in handling dynamic disturbances. When sensors encounter sudden impacts, rapid vibrations, or directional rotational changes during operation, signals are prone to issues such as out-of-order delivery, overlap, amplitude distortion, and path deviation, leading to confusing signal interpretation or increased inter-channel interference. This is especially problematic in multi-channel collaborative applications, where coupling disturbances between signal sources further amplify signal distortion. Summary of the Invention

[0003] Based on this, the present invention provides a sensor signal buffering method and system for high-impact scenarios to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objective, a sensor signal buffering method for high-impact scenarios includes the following steps:

[0005] Step S1: Acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data;

[0006] Step S2: Perform sensor impact identification based on sensor operating data to generate sensor impact data; perform sensor collision and signal coupling disturbance analysis based on sensor impact data and sensor signal status data to generate sensor collision-signal coupling disturbance data;

[0007] Step S3: Perform multi-channel collaborative buffering of sensor signals based on sensor collision-signal coupling disturbance data to generate multi-channel collaborative buffered data of sensor signals;

[0008] Step S4: Analyze the fluctuation and attenuation of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal, and generate sensor buffer signal fluctuation-attenuation data;

[0009] Step S5: Perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data to generate sensor signal buffer stabilization-enhancement data.

[0010] Furthermore, step S1 includes the following steps:

[0011] Step S11: Acquire sensor operating data, and perform sensor signal noise reduction and baseline correction processing based on the sensor operating data to generate sensor signal noise reduction-baseline correction data;

[0012] Step S12: Perform time-domain and frequency-domain analysis of the sensor signal based on the sensor signal noise reduction-baseline correction data to generate sensor signal time-domain-frequency-domain data;

[0013] Step S13: Perform sensor signal phase synchronization calibration based on sensor signal time-frequency domain data to generate sensor signal phase synchronization data;

[0014] Step S14: Based on the sensor signal phase synchronization data, identify the sensor signal status and generate sensor signal status data.

[0015] Furthermore, step S2 includes the following steps:

[0016] Step S21: Perform sensor impact identification based on sensor operation data, generate sensor impact data, and perform sensor impact characteristic analysis based on sensor impact data to generate sensor impact characteristic data.

[0017] Step S22: Extract sensor signal features based on sensor signal status data to generate sensor signal feature data;

[0018] Step S23: Analyze the multi-channel connectivity of sensor signals based on the sensor signal characteristic data to generate multi-channel connectivity data of sensor signals;

[0019] Step S24: Based on the sensor's impact characteristic data and the sensor signal multi-channel connectivity data, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data.

[0020] Furthermore, step S24 includes the following steps:

[0021] Step S241: Based on the impact characteristic data of the sensor, determine the impact intensity and angle of the sensor, and generate the impact intensity-angle data of the sensor.

[0022] Step S242: Based on the impact intensity-angle data of the sensor, identify the vibration frequency and rotation change of the sensor, and generate vibration frequency-rotation change data;

[0023] Step S243: Analyze the multi-signal switching frequency and channel path of the sensor based on the multi-channel connectivity data of the sensor signal, and generate multi-signal switching frequency-channel path data;

[0024] Step S244: Based on the vibration frequency-rotation change data, perform multi-channel phase drift analysis of sensor signals on the multi-signal switching frequency-channel path data to generate multi-channel phase drift data of sensor signals;

[0025] Step S245: Based on the vibration frequency-rotation change data and the multi-channel phase drift data of the sensor signal, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data.

[0026] Furthermore, step S3 includes the following steps:

[0027] Step S31: Perform sensor signal disturbance timing analysis based on sensor impact-signal coupling disturbance data to generate sensor signal disturbance timing data;

[0028] Step S32: Based on the sensor signal disturbance time series data, identify the signal transmission and reception position changes and signal overlap corresponding to the sensor impact, and generate signal transmission and reception position change-signal overlap data;

[0029] Step S33: Perform multi-signal misorder analysis corresponding to sensor impact based on signal transmission and reception position changes and signal overlap data to generate multi-signal misorder data corresponding to sensor impact.

[0030] Step S34: Perform multi-channel collaborative buffering of sensor signals based on signal transmission and reception changes, signal overlap data, and multi-signal out-of-order data corresponding to sensor impact, to generate multi-channel collaborative buffered data of sensor signals.

[0031] Furthermore, step S33 includes the following steps:

[0032] Step S331: Based on the signal transmission and reception position change-signal overlap data, identify the sensor signal frequency and path change, and generate sensor signal frequency-path change data;

[0033] Step S332: Based on the sensor signal frequency-path change data, perform sensor signal and channel cross-interference mapping identification to generate sensor signal-channel cross-interference mapping data;

[0034] Step S333: Perform sensor signal and channel aliasing and recombination analysis based on sensor signal-channel cross-interference mapping data to generate sensor signal-channel aliasing and recombination data;

[0035] Step S334: Perform multi-signal misorder analysis corresponding to sensor impact based on sensor signal-channel aliasing and recombination data to generate multi-signal misorder data corresponding to sensor impact.

[0036] Furthermore, step S34 includes the following steps:

[0037] Step S341: Perform sensor signal separation and spectrum reconstruction processing based on the signal transmission and reception changes and signal overlap data to generate sensor signal separation-spectrum reconstruction data;

[0038] Step S342: Perform sensor signal timing recovery and channel alignment processing based on the multi-signal misordered data corresponding to the sensor impact to generate sensor signal timing recovery-channel alignment data;

[0039] Step S343: Based on the sensor signal separation-spectrum reconstruction data and the sensor signal timing recovery-channel alignment data, perform disturbance fluctuation characteristics analysis of the sensor signal under the corresponding impact state, and generate disturbance fluctuation characteristic data of the sensor signal under the corresponding impact state;

[0040] Step S344: Based on the disturbance fluctuation characteristic data of the sensor signal under the corresponding impact state, perform multi-channel collaborative buffering processing on the sensor signal timing recovery-channel alignment data to generate multi-channel collaborative buffered data of the sensor signal.

[0041] Furthermore, step S4 includes the following steps:

[0042] Step S41: Perform time-domain and waveform decomposition of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal to generate time-domain-waveform data of the sensor buffer signal;

[0043] Step S42: Analyze the peak attenuation and oscillation period of the sensor signal based on the time-domain waveform data of the sensor buffer signal, and generate sensor signal peak attenuation-oscillation period data;

[0044] Step S43: Detect the nonlinear fluctuation characteristics of the sensor buffer signal based on the peak attenuation-oscillation period data of the sensor signal, and generate nonlinear fluctuation characteristic data of the sensor buffer signal;

[0045] Step S44: Based on the nonlinear fluctuation characteristic data of the sensor buffer signal and the peak attenuation-oscillation period data of the sensor signal, perform fluctuation and attenuation analysis of the sensor buffer signal to generate sensor buffer signal fluctuation-attenuation data.

[0046] Furthermore, step S5 includes the following steps:

[0047] Step S51: Perform time-frequency domain mapping and transformation processing on the sensor signal based on the sensor buffer signal fluctuation-attenuation data to generate sensor signal time-frequency domain mapping and transformation data;

[0048] Step S52: Perform sensor multi-signal and channel feature fusion processing based on the sensor signal time-frequency domain mapping conversion data to generate sensor multi-signal-channel feature fusion data;

[0049] Step S53: Perform sensor signal buffer stabilization and enhancement processing based on sensor signal time-frequency domain mapping transformation data and sensor multi-signal-channel feature fusion data to generate sensor signal buffer stabilization-enhancement data.

[0050] Furthermore, the present invention also provides a sensor signal buffering system for high-impact scenarios, for performing the sensor signal buffering method for high-impact scenarios as described above, the sensor signal buffering system for high-impact scenarios comprising:

[0051] The signal recognition module is used to acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data.

[0052] The collision and signal coupling analysis module is used to identify sensor impacts based on sensor operating data and generate sensor impact data; and to perform sensor collision and signal coupling disturbance analysis based on sensor impact data and sensor signal status data to generate sensor collision-signal coupling disturbance data.

[0053] The signal buffer module is used to perform multi-channel collaborative buffering of sensor signals based on sensor impact-signal coupling disturbance data, and generate multi-channel collaborative buffered sensor signal data.

[0054] The buffer signal fluctuation and attenuation analysis module is used to perform sensor buffer signal fluctuation and attenuation analysis based on multi-channel collaborative buffer data of sensor signals, and generate sensor buffer signal fluctuation-attenuation data.

[0055] The buffer signal stabilization and enhancement module is used to perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data, and generate sensor signal buffer stabilization-enhancement data.

[0056] The beneficial effects of this invention are:

[0057] 1. The sensor signal buffering method for high-impact scenarios proposed in this invention, compared with the prior art, has the following advantages: It acquires sensor operating data, enabling real-time capture of the sensor's original operating state, including key parameters such as signal strength, frequency, and phase. Based on the sensor operating data, it identifies the sensor signal state, promptly determining whether the sensor signal is within its normal operating range and whether there are abnormal fluctuations or distortions. Furthermore, it performs sensor impact identification based on the sensor operating data, accurately capturing the external force impact on the sensor, including the impact's force, direction, and duration. Based on the sensor impact data and sensor signal state data, it analyzes sensor collision and signal coupling disturbances, deeply analyzing the interaction between impact and signal, quantifying the intensity, frequency characteristics, and degree of influence of the disturbance on the signal. Based on sensor impact-signal coupling disturbance data, multi-channel collaborative buffering processing of sensor signals is performed to improve the stability of sensor signals in complex interference environments. By integrating signal resources from multiple channels, each channel works in concert, dynamically adjusting signal gain and filtering parameters to jointly offset signal fluctuations caused by impacts, achieving comprehensive suppression of disturbance signals. Furthermore, collaborative buffering processing can utilize the correlation between channels for cross-validation and compensation, reducing errors that may arise from single-channel processing. It is also adaptable to impact disturbances of varying intensities and types. Based on the multi-channel collaborative buffering data, sensor buffer signal fluctuation and attenuation analysis is performed. Fluctuation analysis accurately quantifies the amplitude, frequency, and period of signal fluctuations after buffering, identifying remaining minute fluctuation components and understanding the actual effect of buffering, thus revealing shortcomings in the processing. Attenuation analysis focuses on the energy attenuation of the signal during transmission and processing, determining the attenuation rate, pattern, and impact on signal integrity, helping to evaluate the protective effect of buffering on signal strength. Finally, sensor signal buffering stabilization and enhancement processing is performed based on sensor buffer signal fluctuation-attenuation data, elevating the multi-round processed signal to its optimal state to meet the high signal quality requirements of practical applications. By combining the signal with the impact situation and performing multi-channel buffering processing when the sensor encounters sudden impact, rapid vibration and directional rotation changes during operation, the problems of signal disorder, overlap, amplitude distortion and path deviation, as well as signal interpretation confusion or inter-channel interference in high impact scenarios are solved.

[0058] 2. The sensor signal buffering system for high-impact scenarios proposed in this invention consists of a signal identification module, an impact and signal coupling analysis module, a signal buffering module, a buffer signal fluctuation attenuation analysis module, and a buffer signal stabilization and enhancement module. It can implement any sensor signal buffering method for high-impact scenarios described in this invention. The system uses the combined operations of computer programs running on each module to implement the sensor signal buffering method for high-impact scenarios. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient sensor signal buffering process for high-impact scenarios, thereby simplifying the operation process of the sensor signal buffering system for high-impact scenarios. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the steps of a sensor signal buffering method for high-impact scenarios according to the present invention.

[0060] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0061] Figure 3 for Figure 2 A detailed flowchart illustrating the implementation steps of step S24.

[0062] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0063] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0064] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0065] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a sensor signal buffering method for high-impact scenarios, comprising the following steps:

[0067] Step S1: Acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data;

[0068] Step S2: Perform sensor impact identification based on sensor operating data to generate sensor impact data; perform sensor collision and signal coupling disturbance analysis based on sensor impact data and sensor signal status data to generate sensor collision-signal coupling disturbance data;

[0069] Step S3: Perform multi-channel collaborative buffering of sensor signals based on sensor collision-signal coupling disturbance data to generate multi-channel collaborative buffered data of sensor signals;

[0070] Step S4: Analyze the fluctuation and attenuation of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal, and generate sensor buffer signal fluctuation-attenuation data;

[0071] Step S5: Perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data to generate sensor signal buffer stabilization-enhancement data.

[0072] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of a sensor signal buffering method for high-impact scenarios according to the present invention. In this example, the sensor signal buffering method for high-impact scenarios includes the following steps:

[0073] Step S1: Acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data;

[0074] In this embodiment of the invention, under high-impact scenarios, a 16-bit high-precision data acquisition card is connected to the sensor output terminal to continuously acquire sensor operating data at a sampling frequency of 10kHz for a duration of 5 minutes. After acquisition, the data is imported into a signal analysis module equipped with a Butterworth low-pass filter, with the filter cutoff frequency set to 1kHz to filter high-frequency noise. Then, a signal state recognition algorithm is activated. By calculating the mean and standard deviation of the signal amplitude, a threshold range is set: when the signal amplitude is within the range of mean ± 2 times the standard deviation, it is considered a normal state; when it exceeds this range and the duration exceeds 10ms, it is marked as an abnormal fluctuation state; when the amplitude is below 1 / 3 of the mean and the duration exceeds 50ms, it is considered a signal interruption state. Simultaneously, signal frequency features are extracted, and the time-domain signal is converted to the frequency domain using Fourier transform. When the main frequency deviates from the initial calibration value by ±5Hz, it is marked as a frequency drift state. Finally, sensor signal state data containing timestamps, signal amplitudes, and state labels is generated.

[0075] Step S2: Perform sensor impact identification based on sensor operating data to generate sensor impact data; perform sensor collision and signal coupling disturbance analysis based on sensor impact data and sensor signal status data to generate sensor collision-signal coupling disturbance data;

[0076] In this embodiment of the invention, based on the collected raw operating data, an impact identification module is activated. This module has a built-in acceleration threshold detection algorithm, and the impact judgment threshold is set to 500g (gravitational acceleration). When the acceleration value corresponding to the sensor output signal exceeds this threshold and the duration is within the range of 1ms to 100ms, the impact occurrence time, peak acceleration, and duration are recorded to generate sensor impact data. Subsequently, the coupling disturbance analysis module is called to align the impact data with the signal state data on the time axis and calculate the rate of change of signal amplitude from 100ms before the impact to 500ms after the impact. When the rate of change exceeds 5% / ms, coupling disturbance is determined to exist. At the same time, the correlation between the impact signal and the sensor output signal is calculated through a coherence function. When the correlation coefficient is greater than 0.8, it is marked as a strong coupling state; when it is between 0.3 and 0.8, it is marked as a weak coupling state; and when it is less than 0.3, it is determined to be uncoupled. Finally, impact-signal coupling disturbance data containing impact parameters, coupling strength level, and signal distortion degree are generated. The signal distortion degree is calculated by the similarity of the signal waveforms before and after the impact, using the cosine similarity formula, with a value range of 0 to 1. The smaller the value, the more severe the distortion.

[0077] Step S3: Perform multi-channel collaborative buffering of sensor signals based on sensor collision-signal coupling disturbance data to generate multi-channel collaborative buffered data of sensor signals;

[0078] In this embodiment of the invention, for a multi-channel sensor system operating under high-impact scenarios, eight parallel signal processing channels are deployed, each equipped with an independent operational amplifier and a programmable gain controller. Upon receiving sensor impact-signal coupling disturbance data, a collaborative buffering mechanism is activated. When a channel detects a strong coupling disturbance, inter-channel data exchange is automatically triggered, allocating the signal from that channel to the other three idle channels for parallel processing. Each channel employs an adaptive RC buffer circuit with a fixed resistance of 10kΩ and a capacitance value dynamically adjusted according to the disturbance intensity: 10μF in strong coupling and 1μF in weak coupling, with the capacitor connection state switched via a relay. Simultaneously, an inter-channel signal calibration mechanism is activated, using the signal from the unaffected channel as a reference to correct the amplitude of the disturbed channel's signal. The correction coefficient is calculated using the least squares method, ensuring that the signal error of each channel is controlled within ±2%. After processing, the data from the eight channels are time-aligned and merged into multi-channel collaborative buffered data according to timestamps.

[0079] Step S4: Analyze the fluctuation and attenuation of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal, and generate sensor buffer signal fluctuation-attenuation data;

[0080] In this embodiment of the invention, a fluctuation analysis module is invoked to process multi-channel collaborative buffered data. A sliding window method is used, with a window size of 100 sampling points and a sliding step size of 10 sampling points. The variance of the signal within each window is calculated. When the variance exceeds 0.01V for three consecutive windows, it is marked as a fluctuation interval. Simultaneously, the peak and trough values ​​of the signal within the fluctuation interval are extracted, and the peak-to-peak value is calculated. When the peak-to-peak value exceeds 50mV, the fluctuation intensity level is recorded as high; between 10mV and 50mV, it is marked as medium; and below 10mV, it is marked as low. For attenuation analysis, the initial amplitude of the stable segment of the buffered signal is selected as the benchmark. The amplitude attenuation is calculated every 100ms. The attenuation coefficient is obtained through linear fitting. When the absolute value of the attenuation coefficient exceeds 0.05V / 100ms, it is judged as rapid attenuation; between 0.01V / 100ms and 0.05V / 100ms, it is marked as slow attenuation; and below 0.01V / 100ms, it is judged as no significant attenuation. Finally, buffered signal fluctuation-attenuation data containing the start and end times of the fluctuation range, peak-to-peak value, and attenuation coefficient are generated.

[0081] Step S5: Perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data to generate sensor signal buffer stabilization-enhancement data.

[0082] In this embodiment of the invention, after receiving the fluctuation-attenuation data of the sensor buffer signal, a stabilization processing module is activated. For the high fluctuation level range, a Kalman filter is used to smooth the signal. The noise variance during the filter process is set to 0.01, and the measurement noise variance is dynamically adjusted according to the fluctuation intensity: 0.1 for high fluctuation, 0.05 for medium fluctuation, and 0.02 for low fluctuation. Simultaneously, a signal enhancement circuit is activated for the rapid attenuation range. This circuit includes two operational amplifier stages. The first stage gain is set to 5 times, and the second stage gain is adjusted according to the attenuation coefficient: 3 times for an attenuation coefficient of 0.05V / 100ms; 2 times for 0.03V / 100ms; and 1.5 times for 0.01V / 100ms. During the enhancement process, a hardware limiting circuit restricts the signal amplitude to the range of 0 to 5V to avoid signal saturation. After processing, the signal stability index is calculated, which is the ratio of the change in signal amplitude within 500ms to the initial amplitude. When this ratio is less than 1%, it is considered to be in a stable state. Simultaneously, the signal-to-noise ratio (SNR) after signal enhancement is calculated to ensure that the SNR is greater than 30dB. Finally, sensor signal buffer stabilization-enhancement data is generated.

[0083] Furthermore, step S1 includes the following steps:

[0084] Step S11: Acquire sensor operating data, and perform sensor signal noise reduction and baseline correction processing based on the sensor operating data to generate sensor signal noise reduction-baseline correction data;

[0085] In this embodiment of the invention, under high-impact scenarios, a 24-bit data acquisition card is connected to the sensor output to continuously acquire sensor operating data for 6 minutes at a sampling frequency of 20kHz. The acquired raw data is then fed into a hardware noise reduction circuit, which includes a three-stage filtering module: the first stage is a passive RC low-pass filter with a resistance of 20kΩ, a capacitance of 0.01μF, and a cutoff frequency of 800Hz to filter high-frequency impact noise; the second stage is an active bandpass filter with a center frequency of 50Hz and a bandwidth of ±10Hz to suppress power frequency interference; the third stage is an adaptive noise canceller that eliminates random noise with amplitudes between 5mV and 50mV through correlation calculations with a reference noise source. After noise reduction is completed, a baseline correction module is activated. First, the average amplitude of the signal during the non-impact period (amplitude fluctuation less than 2mV within 10 seconds) is extracted as the reference baseline. Then, a hardware adder is used to offset the signal throughout the entire process. When the signal baseline deviates from the reference value by more than 3mV, a compensation voltage is automatically injected to control the deviation between the corrected signal baseline and the reference value within ±0.5mV. The final result is sensor signal noise reduction and baseline correction data.

[0086] Step S12: Perform time-domain and frequency-domain analysis of the sensor signal based on the sensor signal noise reduction-baseline correction data to generate sensor signal time-domain-frequency-domain data;

[0087] In this embodiment of the invention, based on sensor signal noise reduction and baseline correction data, a time-domain analysis module is activated. This module synchronously triggers sampling through a hardware timer, calculating the signal peak, valley, average value, and kurtosis within every 100ms window. The difference between the peak and valley values ​​must be controlled within 1V, the average value fluctuation range must not exceed ±0.1V, and the kurtosis threshold is set to 3. When the kurtosis is greater than 3, it is marked as an impulse time-domain feature. Simultaneously, a frequency-domain analysis module is activated, using a Fast Fourier Transform chip to convert the time-domain signal into frequency-domain data. The number of transform points is fixed at 4096, the frequency resolution is 0.5Hz, and the analysis frequency band is set to 0 to 1000Hz. Feature frequencies are extracted through spectrum analysis: when the power spectral density of a certain frequency component exceeds 10% of the total power and the duration exceeds 20ms, it is marked as the dominant frequency; when the power ratio of adjacent frequency components exceeds 5:1, it is determined to be a harmonic feature. Time-domain and frequency-domain data are aligned with timestamps to generate time-domain-frequency data containing time-domain parameters (peak, valley, kurtosis labels) and frequency-domain parameters (dominant frequency, harmonic frequency, power percentage). A comprehensive record is generated every 200ms. The frequency-domain data needs to be labeled with the phase information of each frequency component, and the phase error is controlled within ±5°.

[0088] Step S13: Perform sensor signal phase synchronization calibration based on sensor signal time-frequency domain data to generate sensor signal phase synchronization data;

[0089] In this embodiment of the invention, after receiving the time-frequency domain data of the sensor signal, a phase synchronization calibration system is activated. This system includes four synchronous sampling channels and one phase comparator. Channel 1 is first selected as the reference channel, and its signal phase value at the 50Hz main frequency is extracted as the reference phase. The reference phase is locked using a phase-locked loop circuit, with the locking error controlled within ±0.1°. Subsequently, the 50Hz main frequency phase of the other three channels is compared with the reference phase in real time. When the phase difference exceeds ±10°, a phase compensation mechanism is activated: the signal is delayed using an adjustable delay line circuit with a delay step of 10ns. The phase difference is detected after each adjustment until the phase difference of all channels is controlled within ±2°. For non-main frequency components (such as 100Hz and 150Hz harmonics), the same method is used for phase calibration. After calibration, the phase difference of the same frequency components in each channel does not exceed ±5°. During the calibration process, a phase error report is generated every 10ms. When the phase error of three consecutive reports is less than ±3°, it is determined to be in a stable synchronization state. The final generated phase synchronization data includes the phase value of each channel, the amount of compensation delay, and the synchronization status label.

[0090] Step S14: Based on the sensor signal phase synchronization data, identify the sensor signal status and generate sensor signal status data.

[0091] In this embodiment of the invention, a signal state recognition hardware module is activated based on the sensor signal phase synchronization data. This module includes three parallel detection units. The amplitude detection unit sets a threshold range: 0.2V to 0.8V for normal operation. When the signal amplitude exceeds 0.8V and the duration is ≥5ms, it is marked as overshoot; when it is below 0.2V and the duration is ≥10ms, it is marked as undershoot. The frequency detection unit tracks the signal's main frequency through a phase-locked loop. When the main frequency deviates from the calibration value (50Hz) ±2Hz and the duration is ≥20ms, it is marked as frequency offset. The phase detection unit monitors the phase difference of each channel in real time. When the phase difference between any two channels exceeds ±5° and the duration is ≥15ms, it is marked as phase mismatch. Simultaneously, a state fusion mechanism is activated. When a certain state label appears three times consecutively (each time with a 10ms interval), it is confirmed as a valid state. If two or more state labels appear simultaneously, they are marked according to the priority order: overshoot > phase mismatch > frequency offset > undershoot. The final generated signal state data is updated every 10ms, including state label, state duration and corresponding amplitude, frequency and phase parameters, and is stored in a circular buffer to ensure continuous data without loss.

[0092] Furthermore, step S2 includes the following steps:

[0093] Step S21: Perform sensor impact identification based on sensor operation data, generate sensor impact data, and perform sensor impact characteristic analysis based on sensor impact data to generate sensor impact characteristic data.

[0094] Step S22: Extract sensor signal features based on sensor signal status data to generate sensor signal feature data;

[0095] Step S23: Analyze the multi-channel connectivity of sensor signals based on the sensor signal characteristic data to generate multi-channel connectivity data of sensor signals;

[0096] Step S24: Based on the sensor's impact characteristic data and the sensor signal multi-channel connectivity data, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data.

[0097] As an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0098] Step S21: Perform sensor impact identification based on sensor operation data, generate sensor impact data, and perform sensor impact characteristic analysis based on sensor impact data to generate sensor impact characteristic data.

[0099] In this embodiment of the invention, sensor operating data is used and connected to an impact recognition hardware module. This module includes an acceleration threshold comparison circuit and a time counter. The impact recognition threshold is set to 800g. When the converted acceleration value of the sensor output signal exceeds 800g, the counter is triggered to record the impact start time. When the acceleration value falls below 50% of the threshold, the impact end time is recorded. The difference between the two is the impact duration, which is required to be within the range of 0.5ms to 200ms. Simultaneously, a peak hold circuit captures the maximum acceleration value during the impact process, with accuracy controlled within ±5g. The generated impact data includes three parameters: impact occurrence timestamp, peak acceleration, and duration. The impact feature analysis module is then activated to calculate the impact rise slope (the ratio of peak acceleration to rise time). Impacts exceeding 1000 g / ms are classified as steep impacts. The impact pulse width (the time during which the acceleration value exceeds a threshold of 70%) is also calculated; impacts with a pulse width to duration ratio less than 0.3 are classified as narrow-pulse impacts. Fourier transform is used to extract the impact spectrum features; impacts with frequency components above 1000 Hz accounting for more than 30% of the energy are classified as high-frequency impacts. Finally, the sensor's impact feature data is generated and stored in conjunction with the impact data, with each record corresponding to a complete impact event, arranged chronologically.

[0100] Step S22: Extract sensor signal features based on sensor signal status data to generate sensor signal feature data;

[0101] In this embodiment of the invention, a signal feature extraction hardware unit is activated based on sensor signal state data. This unit includes an amplitude feature extraction circuit, a frequency feature extraction circuit, and a phase feature extraction circuit. The amplitude feature extraction circuit calculates the peak-to-peak value (difference between peak and trough) and the effective value (square root of the average of the squared values) of the signal within every 50ms window. The peak-to-peak value must be controlled within the range of 0.5V to 2V, and the effective value fluctuation range must not exceed ±0.2V. The frequency feature extraction circuit tracks the signal's main frequency through a phase-locked loop, recording the main frequency value and its stability (the change in main frequency within 100ms). The main frequency stability must be less than ±1Hz. The phase feature extraction circuit calculates the average phase difference between each channel for the calibrated multi-channel signal, and the average value must be controlled within ±3°. Simultaneously, the waveform features of the signal are extracted, and the signal is compared with a standard sine wave using a hardware comparator to calculate the waveform distortion rate (the ratio of non-sinusoidal components to the fundamental component). When the distortion rate exceeds 5%, it is marked as a waveform distortion feature. The generated signal feature data is stored in time series, with one record generated every 100ms, including amplitude, frequency, phase-related feature parameters and waveform distortion labels.

[0102] Step S23: Analyze the multi-channel connectivity of sensor signals based on the sensor signal characteristic data to generate multi-channel connectivity data of sensor signals;

[0103] In this embodiment of the invention, sensor signal characteristic data is received and connected to a multi-channel connectivity analysis hardware system. This system includes four signal input interfaces, a cross-correlation calculation circuit, and a connectivity strength measurement circuit. Channel 1 is selected as the reference channel, and its signal characteristic data, along with those of channels 2, 3, and 4, are input to the cross-correlation calculation circuit. The calculation window is set to 200ms, and the sliding step is 50ms. The cross-correlation coefficient between each channel and the reference channel is calculated. A coefficient greater than 0.8 is marked as strong connectivity, between 0.3 and 0.8 as weak connectivity, and less than 0.3 as no connectivity. Simultaneously, the connectivity delay time (the time difference between the peak values ​​of the two channel signals) is calculated through the connectivity strength measurement circuit. A delay time exceeding 10ms is marked as delayed connectivity. Connectivity analysis is performed for different signal characteristics: amplitude characteristic connectivity is based on the consistency of peak-to-peak value variation trends, frequency characteristic connectivity is based on the synchronicity of dominant frequency changes, and phase characteristic connectivity is based on the stability of phase difference. The generated multi-channel connectivity data includes four parameters: channel pairs, number of mutual relationships, connectivity type, and latency time. It is updated every 500ms and stored in categories according to channel pairs.

[0104] Step S24: Based on the sensor's impact characteristic data and the sensor signal multi-channel connectivity data, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data.

[0105] In this embodiment of the invention, the impact characteristic data of the sensor and the multi-channel connectivity data of the sensor signal are invoked to start the coupling disturbance analysis hardware platform. This platform includes a time axis alignment module, a disturbance propagation path analysis module, and a coupling strength calculation module. The time axis alignment module matches the impact occurrence time with the signal characteristic change time. When the signal characteristic change occurs within 0 to 50 ms after the impact, it is determined to be a potential coupling relationship. The disturbance propagation path analysis module, based on the multi-channel connectivity, marks a direct propagation path when the impact channel has a strong connectivity with other channels and the delay time is less than 5 ms; and marks an indirect propagation path when there is a weak connectivity and the delay time is between 5 ms and 20 ms. The coupling strength calculation module calculates the ratio of impact characteristic parameters to signal characteristic changes. When the ratio of amplitude change to peak impact acceleration exceeds 0.001V / g, it is marked as strong coupling; when the ratio of frequency offset to impact duration exceeds 0.1Hz / ms, it is marked as frequency coupling; and when the ratio of phase difference change to impact rise slope exceeds 0.01° / (g / ms), it is marked as phase coupling. The final generated impact-signal coupling disturbance data includes five parameters: impact event ID, coupling channel pair, transmission path, coupling type, and coupling strength, forming an associated index with the impact characteristic data and connectivity data.

[0106] Furthermore, step S24 includes the following steps:

[0107] Step S241: Based on the impact characteristic data of the sensor, determine the impact intensity and angle of the sensor, and generate the impact intensity-angle data of the sensor.

[0108] Step S242: Based on the impact intensity-angle data of the sensor, identify the vibration frequency and rotation change of the sensor, and generate vibration frequency-rotation change data;

[0109] Step S243: Analyze the multi-signal switching frequency and channel path of the sensor based on the multi-channel connectivity data of the sensor signal, and generate multi-signal switching frequency-channel path data;

[0110] Step S244: Based on the vibration frequency-rotation change data, perform multi-channel phase drift analysis of sensor signals on the multi-signal switching frequency-channel path data to generate multi-channel phase drift data of sensor signals;

[0111] Step S245: Based on the vibration frequency-rotation change data and the multi-channel phase drift data of the sensor signal, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data.

[0112] As an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 2 A detailed flowchart of step S24 is shown. In this embodiment, step S24 includes the following steps:

[0113] Step S241: Based on the impact characteristic data of the sensor, determine the impact intensity and angle of the sensor, and generate the impact intensity-angle data of the sensor.

[0114] In this embodiment of the invention, an impact intensity and angle positioning hardware system is activated based on the impact characteristic data from sensors. This system includes a three-dimensional accelerometer array (composed of four accelerometers distributed at the vertices of a regular tetrahedron) and a data fusion circuit. Impact intensity is calculated by averaging the peak accelerations detected by the four sensors, with an accuracy controlled within ±10g. An average value exceeding 1000g is marked as an extremely strong impact, 500g to 1000g as a strong impact, 200g to 500g as a moderate impact, and below 200g as a weak impact. Angle positioning employs a triangulation algorithm. By calculating the time difference between the impact signals detected by the four sensors (accuracy ±0.1ms) and combining it with the spatial coordinates of the sensor array (with a fixed spacing of 10cm), the impact incident angle is calculated. The angle measurement range is 0° to 180°, with an accuracy of ±2°. An angle less than 30° between the impact angle and the sensor normal direction is marked as a direct impact, 30° to 60° as an oblique impact, and greater than 60° as a lateral impact. Each record of the generated impact intensity-angle data contains an impact intensity value, an incident angle, and an angle type label, and is associated with the impact feature data via a timestamp.

[0115] Step S242: Based on the impact intensity-angle data of the sensor, identify the vibration frequency and rotation change of the sensor, and generate vibration frequency-rotation change data;

[0116] In this embodiment of the invention, impact intensity-angle data from a sensor is received and connected to a vibration frequency and rotation change identification module. This module includes a piezoelectric vibration sensor and a gyroscope. Vibration frequency identification is achieved through a spectrum analysis circuit. A Fourier transform is performed on the vibration signal after impact (2048 transform points, 1Hz frequency resolution), extracting the dominant vibration frequency within the 10Hz to 1000Hz frequency band. When the duration of the dominant frequency exceeds 50ms and the power spectral density exceeds 20% of the total power, it is recorded as a characteristic vibration frequency. Rotation change identification uses a gyroscope to detect changes in angular velocity after impact. The sampling frequency is 1kHz, the measurement range is ±500° / s, and the rotation angle is calculated by integrating the angular velocity every 10ms. When the rotation angle exceeds 5° and the duration exceeds 10ms, it is marked as a rotation change event. Simultaneously, the correlation between vibration frequency and rotation angle is calculated. When the correlation coefficient exceeds 0.6, it is determined to be a rotation change caused by resonance. The generated vibration frequency-rotation change data includes the characteristic vibration frequency, rotation angle, and change duration, and is stored in the order of impact events.

[0117] Step S243: Analyze the multi-signal switching frequency and channel path of the sensor based on the multi-channel connectivity data of the sensor signal, and generate multi-signal switching frequency-channel path data;

[0118] In this embodiment of the invention, a multi-signal switching frequency and channel path analysis unit is activated based on the multi-channel connectivity data of sensor signals. This unit includes a channel switching detector and a path tracking circuit. The multi-signal switching frequency is calculated by detecting the time interval between signal switching between channels. When a signal in one channel changes from valid to invalid and another signal in another channel changes from invalid to valid, the switching time is recorded, and the average time interval of consecutive switching is calculated to obtain the switching frequency, which ranges from 0.1Hz to 10Hz with an accuracy of ±0.01Hz. Channel path analysis tracks the transmission order of signals between multiple channels and combines the channel physical connection topology (preset as a star topology, with a central node connecting 4 branch nodes). When a signal is transmitted from a branch node to the central node, it is marked as a main path; when it is transmitted from the central node to other branch nodes, it is marked as a distribution path; and when it is transmitted directly between branch nodes, it is marked as a bridging path. When the switching frequency exceeds 5Hz and the proportion of the main path is less than 60%, it is marked as a path disorder state. Each generated multi-signal switching frequency-channel path data entry includes a switching frequency value, the proportion of the main path, and the path type distribution, and is updated every 200ms.

[0119] Step S244: Based on the vibration frequency-rotation change data, perform multi-channel phase drift analysis of sensor signals on the multi-signal switching frequency-channel path data to generate multi-channel phase drift data of sensor signals;

[0120] In this embodiment of the invention, vibration frequency-rotation change data and multi-signal switching frequency-channel path data are invoked to enable a multi-channel phase drift analysis module. This module includes a phase comparator and a delay measurement circuit. Phase drift analysis targets the carrier phase of each channel signal (frequency fixed at 1MHz), calculating the phase drift amount every 10ms by comparing the phase difference before and after the impact (accuracy ±0.01°). When the difference between the vibration frequency and the channel switching frequency is less than 2Hz, the phase drift caused by resonance is analyzed. If the ratio of the phase drift amount to the vibration amplitude exceeds 0.5° / mV, it is marked as resonant phase drift. When the rotation angle exceeds 10°, the phase offset caused by rotation is calculated (each 1° rotation corresponds to a 0.5° phase offset), and compared with the actual measured phase difference. If the difference exceeds 3°, it is marked as abnormal phase drift. The generated multi-channel phase drift data includes the phase drift amount, drift type (resonance / rotation / abnormal), and drift duration for each channel, and is associated with the vibration and switching data via channel IDs.

[0121] Step S245: Based on the vibration frequency-rotation change data and the multi-channel phase drift data of the sensor signal, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data.

[0122] In this embodiment of the invention, a comprehensive analysis platform for impact and signal coupling disturbances is initiated based on vibration frequency-rotation variation data and multi-channel phase drift data of sensor signals. This platform first controls the time axis error of vibration, rotation, and phase drift data to within ±1ms using a time alignment circuit. Then, it calculates the correlation between vibration frequency and phase drift rate (correlation coefficient threshold 0.7). When the correlation coefficient exceeds 0.7, it is determined to be vibration-phase coupling. The ratio of rotation angle to channel phase difference is calculated; when the ratio exceeds 0.2%, it is marked as rotation-phase coupling. Simultaneously, the coupling strength is analyzed: a ratio of phase drift to vibration frequency exceeding 0.1° / Hz indicates strong coupling, 0.05° / Hz to 0.1° / Hz indicates medium coupling, and below 0.05° / Hz indicates weak coupling. The final generated impact-signal coupling disturbance data includes coupling type (vibration-phase / rotation-phase), coupling strength level, and coupling duration, forming a complete correlation index chain with the data from previous steps. Each record corresponds to the full-chain coupling analysis result of an impact event.

[0123] Furthermore, step S3 includes the following steps:

[0124] Step S31: Perform sensor signal disturbance timing analysis based on sensor impact-signal coupling disturbance data to generate sensor signal disturbance timing data;

[0125] In this embodiment of the invention, a sensor signal disturbance time-series analysis system is activated based on sensor impact-signal coupling disturbance data. This system includes a high-precision timestamp generator (accuracy ±10ns) and a disturbance event marking circuit. The coupling strength level and duration in the coupled disturbance data are aligned with the time axis of the original sensor signal. The analysis window is set from 100ms before the impact to 1000ms after the impact, and signal disturbance features are extracted at 1ms intervals. A voltage comparator detects the moment when the signal amplitude deviates from the baseline. When the disturbance amplitude exceeds 0.5V and the duration exceeds 5ms, it is marked as a significant disturbance event. Simultaneously, the occurrence frequency of disturbance events (number of disturbances per unit time) is calculated. When the frequency exceeds 10 times / 100ms, it is determined to be a period of dense disturbance. A time-series correlation algorithm is used to analyze the time difference between the disturbance event and the impact features. When the time difference is less than 20ms, it is marked as a disturbance directly caused by the impact; when it exceeds 50ms, it is marked as a delayed disturbance. The generated disturbance time series data includes the disturbance time, amplitude, duration, and correlation type, and is stored in chronological order, with a time series segment generated every 50ms.

[0126] Step S32: Based on the sensor signal disturbance time series data, identify the signal transmission and reception position changes and signal overlap corresponding to the sensor impact, and generate signal transmission and reception position change-signal overlap data;

[0127] In this embodiment of the invention, sensor signal disturbance timing data is received and connected to a signal transmission / reception position change and signal overlap identification module. This module includes a laser displacement sensor (measurement range 0 to 50 cm, accuracy ±0.1 mm) and a signal overlap detector. Signal transmission / reception position change identification uses the laser displacement sensor to monitor the relative displacement between the sensor transmitter and receiver in real time, with a sampling frequency of 1 kHz. When the displacement change exceeds 1 mm and the duration exceeds 10 ms, the time of position change, displacement amount, and change rate (the ratio of displacement to time) are recorded. Signal overlap identification detects the overlap interval of multi-channel signals on the time axis. When the effective levels (exceeding 0.2 V) of two channel signals overlap for more than 10 ms and the sum of the amplitudes of the overlapping portions exceeds 1.5 times the amplitude of a single signal, it is marked as a signal overlap event. The correlation between the position change rate and the signal overlap duration is calculated. When the correlation coefficient exceeds 0.7, it is determined to be signal overlap caused by position change. The generated signal transmission / reception position change-signal overlap data includes displacement parameters, overlap intervals, and associated markers, and is associated with the disturbance timing data via timestamps.

[0128] Step S33: Perform multi-signal misorder analysis corresponding to sensor impact based on signal transmission and reception position changes and signal overlap data to generate multi-signal misorder data corresponding to sensor impact.

[0129] In this embodiment of the invention, a multi-signal misordering analysis unit is activated based on signal transmission and reception position change-signal overlap data. This unit includes a signal sequence comparator and a misordering determination circuit. The multi-signal misordering analysis uses a preset normal signal sequence (transmitted in the order of channel numbers 1 to 4) as a benchmark. By comparing the actual signal arrival time order at the central node, when the signal from channel 2 arrives before channel 1 with a time difference exceeding 5ms, it is marked as a level 1 misordering; when the signal from channel 3 or 4 arrives before channel 1 with a time difference exceeding 10ms, it is marked as a level 2 misordering. Combined with signal overlap data, when the deviation rate of the signal arrival order from the normal sequence within the overlap interval exceeds 30%, it is marked as an overlap-induced misordering. The correlation between misordering events and position changes is calculated. When the position change exceeds 5mm, the misordering occurrence rate must be controlled within 20%; otherwise, it is marked as a severe misordering state. The generated multi-signal misordering data includes the misordering level, occurrence time, and associated position change amount. The misordering occurrence rate is calculated every 200ms and stored according to impact events.

[0130] Step S34: Perform multi-channel collaborative buffering of sensor signals based on signal transmission and reception changes, signal overlap data, and multi-signal out-of-order data corresponding to sensor impact, to generate multi-channel collaborative buffered data of sensor signals.

[0131] In this embodiment of the invention, signal transmission and reception changes, signal overlap data, and multi-signal misordering data corresponding to sensor impacts are invoked to activate a multi-channel collaborative buffering processing platform for sensor signals. This platform includes an adjustable delay buffer circuit (delay range 0 to 100ms, 100ns increments) and a signal priority arbitrator. For signal transmission and reception position changes, when the displacement exceeds 2mm, the delay buffer circuit compensates for the transmission delay caused by the position change. The compensation amount is calculated as displacement × 0.5ms / mm, ensuring that the arrival time difference of signals from each channel is controlled within ±1ms. For signal overlap events, the priority arbitrator is activated, and priorities are set according to channel numbers (1 > 2 > 3 > 4). During the overlap period, high-priority signals are retained, while low-priority signals are temporarily buffered. The buffering duration is equal to the overlap time + 10ms. For multi-signal misordering, when the misordering level is level two, the channel reordering circuit is activated to rearrange the misordered signals into a normal sequence before outputting them. Simultaneously, a gain adjustment circuit (gain range 0.5 to 2 times) compensates for the signal attenuation caused by the misordering, ensuring that the signal amplitude deviation after rearrangement is less than ±0.1V. The generated multi-channel collaborative buffer data includes buffer delay, priority marker, and rearranged sequence. One frame is output every 100ms, and the intra-frame signal synchronization error is controlled within 500ns.

[0132] Furthermore, step S33 includes the following steps:

[0133] Step S331: Based on the signal transmission and reception position change-signal overlap data, identify the sensor signal frequency and path change, and generate sensor signal frequency-path change data;

[0134] In this embodiment of the invention, a sensor signal frequency and path change identification system is activated based on signal transmission and reception position change-signal overlap data. This system includes a frequency counter (measurement range 1Hz to 10kHz, accuracy ±0.1Hz) and a path tracking circuit. Signal frequency identification uses the frequency counter to count signals within a 50ms window. When the signal period stability (deviation over 10 consecutive cycles) is less than ±1%, the current frequency value is recorded. When the frequency change exceeds 5Hz and the duration exceeds 20ms, it is marked as a frequency jump event. Path change identification combines position change data. When the relative displacement between the transmission and reception positions exceeds 3mm, the path tracking circuit is activated. By detecting the signal attenuation between different channels (attenuation = input voltage - output voltage), when the attenuation change exceeds 0.2V and the ratio of attenuation change to displacement change exceeds 0.05V / mm, a path change is determined. Simultaneously, the time difference between the frequency jump and the path change is calculated. When the time difference is less than 15ms, it is marked as a frequency jump caused by a path change. The generated signal frequency-path change data includes frequency value, transition time, path number, and attenuation amount, and is associated with the location change data through timestamps, with records updated every 100ms.

[0135] Step S332: Based on the sensor signal frequency-path change data, perform sensor signal and channel cross-interference mapping identification to generate sensor signal-channel cross-interference mapping data;

[0136] In this embodiment of the invention, sensor signal frequency-path change data is received and accessed to a sensor signal and channel cross-interference mapping and identification module. This module includes an interference detection circuit and a mapping relationship memory. Cross-interference identification is performed by measuring the crosstalk voltage of each channel at different frequencies. When a 1V signal is input to channel 1, if the crosstalk voltage of channel 2 exceeds 0.1V and the frequency difference between channel 2 and channel 1 is less than 10Hz, it is marked as same-frequency cross-interference. When the frequency difference exceeds 100Hz but the crosstalk voltage exceeds 0.05V, it is marked as different-frequency cross-interference. Based on path change data, when the signal switches from path 1 to path 2, if the crosstalk voltage of channel 3 increases by more than 0.08V compared to before the switch, it is recorded as cross-interference caused by path switching. Interference mapping identification is performed by establishing a three-dimensional mapping table of channel-frequency-interference intensity. When a certain mapping relationship occurs more than 15% of the total number of samples, it is determined to be a stable interference mapping. The generated signal-channel cross-interference mapping data includes the interference source channel, the disturbed channel, the interference voltage, and the frequency range. It is stored according to path change events, and each record is associated with the corresponding frequency-path data.

[0137] Step S333: Perform sensor signal and channel aliasing and recombination analysis based on sensor signal-channel cross-interference mapping data to generate sensor signal-channel aliasing and recombination data;

[0138] In this embodiment of the invention, a sensor signal and channel aliasing recombination analysis unit is activated based on sensor signal-channel cross-interference mapping data. This unit includes an aliasing detection filter (cutoff frequency 5kHz) and recombination logic circuitry. Signal aliasing identification detects overlapping spectral regions of multi-channel signals. When the superposition of the power spectral density of two channel signals in a certain frequency band exceeds twice that of a single channel and lasts for more than 30ms, it is marked as spectral aliasing. Channel aliasing identification analyzes the distribution ratio of signals between channels. When the energy proportion of a signal in a non-predetermined channel exceeds 20% and matches the cross-interference mapping data, it is determined to be channel aliasing. Recombination analysis separates the aliased signals using the recombination logic circuitry. The least squares method is used to calculate the signal component coefficients of each channel. Recombination is completed when the signal-to-noise ratio of each component after separation exceeds 25dB. Simultaneously, the degree of aliasing (the ratio of aliased signal energy to total energy) is calculated. When the ratio exceeds 40%, it is marked as a severe aliasing state. The generated signal-channel aliasing and recombination data includes aliasing frequency bands, component coefficients, and recombined signal amplitude. It is stored in conjunction with cross-interference data, and an analysis result is generated every 200ms.

[0139] Step S334: Perform multi-signal misorder analysis corresponding to sensor impact based on sensor signal-channel aliasing and recombination data to generate multi-signal misorder data corresponding to sensor impact.

[0140] In this embodiment of the invention, sensor signal-channel aliasing and reassembly data is received, and multi-signal misorder analysis corresponding to sensor impact is performed. This analysis unit includes a misorder tracing circuit and a sequence verifier. The multi-signal misorder analysis uses the reassembled signal sequence as a benchmark. When the signal from channel 1 arrives at the central node more than 8ms later than channel 2, and this time difference exceeds 60% of the normal transmission delay (preset to 5ms), it is marked as a level 1 misorder. When the signal from channel 4 arrives before channel 1 and the time difference exceeds 15ms, it is marked as a level 2 misorder. Combining the aliasing and reassembly data, when the aliasing degree exceeds 30%, if the misorder occurrence rate increases by more than 25% compared to when there is no aliasing, it is determined to be a misorder caused by aliasing. By comparing the cross-interference mapping data through the misorder tracing circuit, when the cross-interference voltage of channel 3 to channel 1 exceeds 0.12V, if the number of misorders of the channel 1 signal increases, it is marked as an interference-induced misorder. The generated multi-signal misordered data includes misorder level, associated aliasing frequency, interference source marker, and duration. It is associated with the recombined data through signal ID, arranged in the order of impact events, and the misorder occurrence frequency is counted every 50ms.

[0141] Furthermore, step S34 includes the following steps:

[0142] Step S341: Perform sensor signal separation and spectrum reconstruction processing based on the signal transmission and reception changes and signal overlap data to generate sensor signal separation-spectrum reconstruction data;

[0143] In this embodiment of the invention, a sensor signal separation and spectrum reconstruction processing system is activated based on signal transmission and reception changes and signal overlap data. This system includes a signal separator (with built-in four independent bandpass filters, center frequencies of 100Hz, 200Hz, 300Hz, and 400Hz, each with a bandwidth of ±20Hz) and a spectrum reconstruction circuit. Signal separation processing targets overlapping signals, separating different channel signals by frequency band using bandpass filters. When the power spectral density of a certain channel signal at its corresponding center frequency exceeds 40% of the total power of the overlapping signals, it is determined to be the dominant signal of that channel. After separation, amplitude calibration is performed on each signal. A gain adjustment circuit (adjustment range 0.5 to 2 times) is used to control the deviation between the calibrated signal amplitude and the theoretical amplitude before separation within ±0.05V. Spectrum reconstruction uses inverse Fourier transform to convert the separated frequency domain signal back to the time domain, with 2048 transformation points, ensuring that the waveform distortion rate (deviation from the original signal) of the reconstructed signal is less than 3%. Simultaneously, the matching degree between the reconstructed spectrum and the original spectrum is calculated. When the matching degree exceeds 90%, spectrum reconstruction is completed. The generated signal separation-spectrum reconstruction data includes the amplitude, spectral components, and reconstruction error of each channel signal after separation. It is associated with the overlapping data through timestamps, and a set of data is generated every 100ms.

[0144] Step S342: Perform sensor signal timing recovery and channel alignment processing based on the multi-signal misordered data corresponding to the sensor impact to generate sensor signal timing recovery-channel alignment data;

[0145] In this embodiment of the invention, multi-signal out-of-order data corresponding to sensor impact is received and connected to a sensor signal timing recovery and channel alignment processing module. This module includes a timing recovery circuit (delay adjustment range 0 to 50ms, step 100ns) and a channel alignment detector. Timing recovery processing uses the normal transmission delay (5ms) in the out-of-order data as a benchmark. When the out-of-order time of channel 1 signal exceeds 8ms, a compensation delay is introduced through the timing recovery circuit. The compensation amount is equal to the difference between the out-of-order time and the normal delay, ensuring that the arrival time difference of the recovered signal is controlled within ±1ms. Channel alignment processing compares the rising edge times of each channel signal using the channel alignment detector. When the rising edge time difference between channel 2 and channel 1 exceeds 3ms, a synchronization pulse generator is activated, sending a trigger signal to channel 2 to adjust its sampling time, ensuring that the rising edge time difference of each channel after alignment is less than 0.5ms. Simultaneously, the signal jitter (time deviation within 100ms) after timing recovery is calculated. When the jitter exceeds 0.3ms, a compensation capacitor (0.1μF) is added to stabilize the signal. The generated timing recovery-channel alignment data includes compensation delay, alignment error, and jitter, and is associated with the out-of-order data through a signal ID. The record is updated every 50ms.

[0146] Step S343: Based on the sensor signal separation-spectrum reconstruction data and the sensor signal timing recovery-channel alignment data, perform disturbance fluctuation characteristics analysis of the sensor signal under the corresponding impact state, and generate disturbance fluctuation characteristic data of the sensor signal under the corresponding impact state;

[0147] In this embodiment of the invention, a disturbance fluctuation characteristic analysis unit is activated based on sensor signal separation-spectrum reconstruction data and sensor signal timing recovery-channel alignment data. This unit includes a fluctuation detection circuit and a characteristic parameter calculator. For signals under impact conditions, the disturbance fluctuation characteristic analysis calculates the fluctuation amplitude (difference between peak and trough values) within a 20ms window. When the fluctuation amplitude exceeds 0.3V and the duration exceeds 10ms, it is marked as a significant fluctuation. Frequency components of the fluctuation are extracted through spectrum analysis. When the fluctuation energy in the 100Hz to 500Hz frequency band accounts for more than 40% of the total fluctuation energy, it is marked as a mid-frequency fluctuation characteristic. The ratio of fluctuation amplitude to impact intensity is calculated. When the ratio exceeds 0.001V / g, it is determined to be a severe fluctuation caused by a strong impact. Simultaneously, the correlation between fluctuations in different channels is analyzed. When the correlation coefficient exceeds 0.7, it is marked as a coordinated fluctuation. The generated disturbance fluctuation characteristic data includes fluctuation amplitude, frequency distribution, and impact correlation, and is stored in conjunction with the separation data and timing data. Each record corresponds to the fluctuation characteristics of one impact event.

[0148] Step S344: Based on the disturbance fluctuation characteristic data of the sensor signal under the corresponding impact state, perform multi-channel collaborative buffering processing on the sensor signal timing recovery-channel alignment data to generate multi-channel collaborative buffered data of the sensor signal.

[0149] In this embodiment of the invention, disturbance fluctuation characteristic data and sensor signal timing recovery-channel alignment data of the sensor signal under the corresponding impact state are invoked to activate the multi-channel collaborative buffering processing platform for the sensor signal. This platform includes an adjustable gain buffer (gain range of 0.8 to 1.5 times) and a multi-channel collaborative control circuit. When the disturbance fluctuation amplitude exceeds 0.3V, the adjustable gain buffer automatically reduces the gain to 0.8 times, keeping the buffered signal amplitude below 0.5V; when the fluctuation amplitude is less than 0.1V, the gain is increased to 1.2 times to enhance the signal strength. Based on the collaborative fluctuation marker in the fluctuation characteristic data, the multi-channel collaborative control circuit activates a collective buffering mode when collaborative fluctuations occur in three or more channels, synchronously adjusting the RC buffer network of each channel (resistor 10kΩ, capacitor adjustable from 0.01μF to 0.1μF) to reduce the buffered signal fluctuation amplitude to below 30% of the original value. Simultaneously, the signal-to-noise ratio of the buffered signal is monitored to ensure that the signal-to-noise ratio exceeds 30dB. The generated multi-channel collaborative buffer data includes buffer gain, capacitance value, output amplitude, and signal-to-noise ratio. It is associated with fluctuation characteristic data and time series data through impact event ID, and a frame of buffer results is output every 200ms.

[0150] Furthermore, step S4 includes the following steps:

[0151] Step S41: Perform time-domain and waveform decomposition of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal to generate time-domain-waveform data of the sensor buffer signal;

[0152] In this embodiment of the invention, based on multi-channel collaborative buffered data of sensor signals, a time-domain and waveform decomposition system for sensor buffered signals is enabled. This system includes a time-domain analysis circuit (sampling frequency 10kHz) and a waveform decomposition module (using wavelet decomposition algorithm, with 5 decomposition layers). For each channel of buffered signal, the time-domain analysis extracts the signal's time-domain parameters in a 20ms window: calculating the mean amplitude (accuracy ±0.01V), rising edge slope (the ratio of voltage change to time, unit V / ms), and falling edge slope within the window. When the rising edge slope exceeds 0.5V / ms and the duration exceeds 5ms, it is marked as a steep rising characteristic. Waveform decomposition uses wavelet decomposition to decompose the signal into 5 frequency bands (1Hz-10Hz, 10Hz-100Hz, 100Hz-1kHz, 1kHz-5kHz, 5kHz-10kHz), with the reconstruction error of each decomposition layer controlled within 2%. After decomposition, correlation analysis was performed on the waveforms of each frequency band. When the correlation coefficient between the waveform in the 100Hz-1kHz frequency band and the original waveform exceeded 0.85, it was determined to be the main fluctuation component. The generated buffer signal time-domain waveform data includes the time-domain parameters of each window, the waveform amplitude of the five frequency bands, and the decomposition error. It is associated with the co-buffered data through the impact event ID, and a set of data is generated every 50ms.

[0153] Step S42: Analyze the peak attenuation and oscillation period of the sensor signal based on the time-domain waveform data of the sensor buffer signal, and generate sensor signal peak attenuation-oscillation period data;

[0154] In this embodiment of the invention, time-domain waveform data of the sensor buffer signal is received and connected to a sensor signal peak attenuation and oscillation period analysis module. This module includes a peak detector (response time ≤ 10 μs) and a period counter (measurement range 1 ms-1 s, accuracy ± 0.1 ms). Peak attenuation analysis targets the continuous peaks of the buffer signal. The peak detector records the amplitude of the first 10 peaks after the impact, and calculates the attenuation of adjacent peaks (previous peak minus the next peak). When the ratio of the attenuation to the previous peak exceeds 15%, it is marked as a rapid attenuation stage; when the ratio is less than 5%, it is marked as a slow attenuation stage. Oscillation period analysis measures the time interval between two adjacent peaks (or valleys) using the period counter. When the deviation of three consecutive periods is less than 5%, it is recorded as a stable oscillation period; when the deviation exceeds 10%, it is marked as period disorder. Simultaneously, the peak attenuation rate (the ratio of total attenuation to time) is calculated. When the rate exceeds 0.2 V / ms, a damping resistor (10 kΩ) is added to suppress excessively rapid attenuation. The generated peak decay-oscillation period data includes peak sequence, decay stage, and period value, and is associated with time-domain waveform data through timestamps, with records updated every 100ms.

[0155] Step S43: Detect the nonlinear fluctuation characteristics of the sensor buffer signal based on the peak attenuation-oscillation period data of the sensor signal, and generate nonlinear fluctuation characteristic data of the sensor buffer signal;

[0156] In this embodiment of the invention, based on the peak attenuation-oscillation period data of the sensor signal, a nonlinear fluctuation feature detection unit for the sensor buffer signal is activated. This unit includes a nonlinear detection circuit and a feature extraction filter. Nonlinear fluctuation detection compares the deviation between the actual waveform of the signal and the ideal attenuation curve (decaying exponentially). When the deviation value (the difference between the actual amplitude and the theoretical amplitude) exceeds 0.1V and the duration exceeds 10ms, it is marked as a nonlinear fluctuation point. Nonlinear feature parameters are extracted: the singularity exponent of the fluctuation is calculated (using wavelet transform modulus maxima); when the exponent exceeds 0.8, it is marked as a strong nonlinear feature; the amplitude ratio of adjacent periods is calculated. ( For the first The cycle and the first The amplitude ratio of each cycle, For the first The amplitude of each cycle, For the first The amplitude of each period is measured, and when the standard deviation of the ratio exceeds 0.1, it is marked as amplitude nonlinearity. Simultaneously, the relationship between nonlinear fluctuations and impact intensity is analyzed; when the impact intensity... When the weight exceeds 800g, if the number of nonlinear fluctuation points... The increase of over 30% compared to 500g indicates a nonlinear enhancement induced by high impact. The generated nonlinear fluctuation characteristic data includes deviation values, singularity index, and the time of feature occurrence, which are stored in conjunction with peak decay data, with each record corresponding to one oscillation cycle.

[0157] Step S44: Based on the nonlinear fluctuation characteristic data of the sensor buffer signal and the peak attenuation-oscillation period data of the sensor signal, perform fluctuation and attenuation analysis of the sensor buffer signal to generate sensor buffer signal fluctuation-attenuation data.

[0158] In this embodiment of the invention, the sensor buffer signal nonlinear fluctuation characteristic data and sensor signal peak decay-oscillation period data are invoked to activate the sensor buffer signal fluctuation and decay analysis platform. This platform includes a fluctuation analysis circuit and a decay model calculator. Fluctuation analysis targets nonlinear fluctuation characteristics, calculating the fluctuation amplitude (difference between peak and trough values) within every 50ms window. When the amplitude exceeds 0.3V and contains nonlinear characteristic points, it is marked as a strong fluctuation interval; when the amplitude is below 0.1V and has no nonlinear characteristics, it is marked as a stable interval. Decay analysis combines peak decay data and fits the decay curve using the decay model calculator (using the least squares method to fit an exponential function). When the fitting error (deviation between actual and model values) exceeds 5%, a nonlinear correction term (based on the singularity exponent) is introduced to optimize the model. Simultaneously, the correlation between fluctuation energy and decay rate is calculated; when the correlation coefficient exceeds 0.6, it is determined to be accelerated decay. The generated fluctuation-decay data includes fluctuation amplitude, decay coefficient, and model error, and is associated with the nonlinear characteristics and peak decay data through impact event IDs. A set of comprehensive analysis results is output every 200ms.

[0159] Furthermore, step S5 includes the following steps:

[0160] Step S51: Perform time-frequency domain mapping and transformation processing on the sensor signal based on the sensor buffer signal fluctuation-attenuation data to generate sensor signal time-frequency domain mapping and transformation data;

[0161] In this embodiment of the invention, a sensor signal time-frequency domain mapping and conversion system is enabled based on sensor buffer signal fluctuation-attenuation data. This system includes a short-time Fourier transform module (transformation window length 256ms, overlap rate 50%) and a time-frequency domain mapping memory. The time-frequency domain mapping and conversion process converts the time-domain signal into a two-dimensional time-frequency matrix for each 50ms window of the fluctuation-attenuation data using a short-time Fourier transform. The frequency axis resolution is 1Hz (range 0 to 1000Hz), and the time axis resolution is 10ms. During the conversion, the time-frequency matrix undergoes energy normalization to ensure the maximum energy value is uniformly 1.0, guaranteeing the comparability of data from different windows. When the energy value of a certain time-frequency point exceeds three times the average energy of its corresponding frequency axis, it is marked as a strong energy feature point. Simultaneously, the signal energy error before and after the mapping and conversion is calculated. When the error (the difference between the total energy after conversion and the energy before conversion) exceeds 5%, the transformation window parameters are readjusted. The generated time-frequency domain mapping transformation data includes a time-frequency matrix, coordinates of strong energy feature points, and transformation error. It is associated with the fluctuation-attenuation data through the impact event ID, and a set of transformation results is generated every 100ms.

[0162] Step S52: Perform sensor multi-signal and channel feature fusion processing based on the sensor signal time-frequency domain mapping conversion data to generate sensor multi-signal-channel feature fusion data;

[0163] In this embodiment of the invention, the received sensor signal time-frequency domain mapping conversion data is accessed by a sensor multi-signal and channel feature fusion processing module. This module includes a feature fusion circuit and a channel weight allocator. The feature fusion processing targets the time-frequency features of the multi-channel signals. By calculating the contribution of each channel at high-energy feature points (the ratio of a channel's energy to the total energy of all channels), when the contribution of channel 1 exceeds 30%, it is assigned a weight of 0.4; when the contributions of channels 2 and 3 are between 20% and 30%, they are assigned a weight of 0.25; and when the contribution of channel 4 is below 20%, it is assigned a weight of 0.1. The fusion process uses a weighted summation algorithm to merge the multi-channel time-frequency features into a unified feature matrix. The energy retention rate (ratio to the total energy of each channel) of the fused feature matrix must exceed 90%. Simultaneously, dimensionality reduction processing is performed on the fused features by extracting the first three principal components (with a cumulative contribution rate exceeding 85%) through principal component analysis to reduce data redundancy. The generated multi-signal-channel feature fusion data includes a fusion feature matrix, channel weights, principal component parameters, and is associated with the time-frequency domain transformation data via timestamps, with records updated every 200ms.

[0164] Step S53: Perform sensor signal buffer stabilization and enhancement processing based on sensor signal time-frequency domain mapping transformation data and sensor multi-signal-channel feature fusion data to generate sensor signal buffer stabilization-enhancement data.

[0165] In this embodiment of the invention, based on the time-frequency domain mapping transformation data of sensor signals and the multi-signal-channel feature fusion data of the sensor, a sensor signal buffer stabilization and enhancement processing platform is activated. This platform includes an adaptive stabilization circuit (bandwidth adjustment range 10Hz to 1000Hz) and a signal enhancement amplifier (gain adjustment range 1 to 5 times). The buffer stabilization processing targets strong energy feature points in the time-frequency domain. When the energy fluctuation in a certain frequency band (e.g., 100Hz-200Hz) exceeds 15%, the adaptive stabilization circuit narrows the bandwidth of that frequency band (reducing it to 50% of the original bandwidth), so that the stabilized energy fluctuation is controlled within 5%. The signal enhancement processing, based on the principal component energy in the feature fusion data, activates the enhancement amplifier when the energy of the first principal component is lower than 40% of the total energy, with the gain value adjusted according to... Formula calculation, where To enhance the amplifier's gain, This represents the actual proportion of the energy of the first principal component to the total energy (range 0 to 1). The generated buffered stabilization-enhancement data includes the stabilized bandwidth parameters, enhancement gain value, and signal-to-noise ratio (SNR). It is associated with the preceding data through the impact event ID. A set of processing results is output every 100ms to ensure that the energy proportion of the first principal component after enhancement exceeds 40%. At the same time, the SNR of the processed signal is monitored. When the SNR is lower than 25dB, a low-noise preamplifier (noise figure ≤ 2dB) is added to improve signal quality.

[0166] Furthermore, the present invention also provides a sensor signal buffering system for high-impact scenarios, for performing the sensor signal buffering method for high-impact scenarios as described above, the sensor signal buffering system for high-impact scenarios comprising:

[0167] The signal recognition module is used to acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data.

[0168] The collision and signal coupling analysis module is used to identify sensor impacts based on sensor operating data and generate sensor impact data; and to perform sensor collision and signal coupling disturbance analysis based on sensor impact data and sensor signal status data to generate sensor collision-signal coupling disturbance data.

[0169] The signal buffer module is used to perform multi-channel collaborative buffering of sensor signals based on sensor impact-signal coupling disturbance data, and generate multi-channel collaborative buffered sensor signal data.

[0170] The buffer signal fluctuation and attenuation analysis module is used to perform sensor buffer signal fluctuation and attenuation analysis based on multi-channel collaborative buffer data of sensor signals, and generate sensor buffer signal fluctuation-attenuation data.

[0171] The buffer signal stabilization and enhancement module is used to perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data, and generate sensor signal buffer stabilization-enhancement data.

[0172] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0173] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A sensor signal buffering method for high-impact scenarios, characterized in that, Includes the following steps: Step S1: Acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data; Step S2: Based on the sensor operation data, perform sensor impact identification and generate sensor impact data; based on the sensor impact data and sensor signal status data, perform sensor collision and signal coupling disturbance analysis and generate sensor collision-signal coupling disturbance data. Step S2 includes the following steps: Step S21: Perform sensor impact identification based on sensor operation data, generate sensor impact data, and perform sensor impact characteristic analysis based on sensor impact data to generate sensor impact characteristic data. Step S22: Extract sensor signal features based on sensor signal status data to generate sensor signal feature data; Step S23: Analyze the multi-channel connectivity of sensor signals based on the sensor signal characteristic data to generate multi-channel connectivity data of sensor signals; Step S24: Based on the sensor's impact characteristic data and the sensor signal multi-channel connectivity data, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data; Step S24 includes: Step S241: Based on the impact characteristic data of the sensor, determine the impact intensity and angle of the sensor, and generate the impact intensity-angle data of the sensor. Step S242: Based on the impact intensity-angle data of the sensor, identify the vibration frequency and rotation change of the sensor, and generate vibration frequency-rotation change data; Step S243: Analyze the multi-signal switching frequency and channel path of the sensor based on the multi-channel connectivity data of the sensor signal, and generate multi-signal switching frequency-channel path data; Step S244: Based on the vibration frequency-rotation change data, perform multi-channel phase drift analysis of sensor signals on the multi-signal switching frequency-channel path data to generate multi-channel phase drift data of sensor signals; Step S245: Based on the vibration frequency-rotation change data and the multi-channel phase drift data of the sensor signal, perform sensor impact and signal coupling disturbance analysis to generate sensor impact-signal coupling disturbance data; Step S3: Perform multi-channel collaborative buffering of sensor signals based on sensor collision-signal coupling disturbance data to generate multi-channel collaborative buffered data of sensor signals; Step S3 includes the following steps: Step S31: Perform sensor signal disturbance timing analysis based on sensor impact-signal coupling disturbance data to generate sensor signal disturbance timing data; Step S32: Based on the sensor signal disturbance time series data, identify the signal transmission and reception position changes and signal overlap corresponding to the sensor impact, and generate signal transmission and reception position change-signal overlap data; Step S33: Perform multi-signal misorder analysis corresponding to sensor impact based on signal transmission and reception position changes and signal overlap data to generate multi-signal misorder data corresponding to sensor impact. Step S34: Based on the signal transmission and reception position change-signal overlap data and the multi-signal disordered data corresponding to sensor impact, perform multi-channel collaborative buffering of sensor signals to generate multi-channel collaborative buffered data of sensor signals. Step S4: Analyze the fluctuation and attenuation of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal, and generate sensor buffer signal fluctuation-attenuation data; Step S5: Perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data to generate sensor signal buffer stabilization-enhancement data.

2. The sensor signal buffering method for high-impact scenarios according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Acquire sensor operating data, and perform sensor signal noise reduction and baseline correction processing based on the sensor operating data to generate sensor signal noise reduction-baseline correction data; Step S12: Perform time-domain and frequency-domain analysis of the sensor signal based on the sensor signal noise reduction-baseline correction data to generate sensor signal time-domain-frequency-domain data; Step S13: Perform sensor signal phase synchronization calibration based on sensor signal time-frequency domain data to generate sensor signal phase synchronization data; Step S14: Based on the sensor signal phase synchronization data, identify the sensor signal status and generate sensor signal status data.

3. The sensor signal buffering method for high-impact scenarios according to claim 1, characterized in that, Step S33 includes the following steps: Step S331: Based on the signal transmission and reception position change-signal overlap data, identify the sensor signal frequency and path change, and generate sensor signal frequency-path change data; Step S332: Based on the sensor signal frequency-path change data, perform sensor signal and channel cross-interference mapping identification to generate sensor signal-channel cross-interference mapping data; Step S333: Perform sensor signal and channel aliasing and recombination analysis based on sensor signal-channel cross-interference mapping data to generate sensor signal-channel aliasing and recombination data; Step S334: Perform multi-signal misorder analysis corresponding to sensor impact based on sensor signal-channel aliasing and recombination data to generate multi-signal misorder data corresponding to sensor impact.

4. The sensor signal buffering method for high-impact scenarios according to claim 1, characterized in that, Step S34 includes the following steps: Step S341: Perform sensor signal separation and spectrum reconstruction processing based on the signal transmission and reception position change-signal overlap data to generate sensor signal separation-spectrum reconstruction data; Step S342: Perform sensor signal timing recovery and channel alignment processing based on the multi-signal misordered data corresponding to the sensor impact to generate sensor signal timing recovery-channel alignment data; Step S343: Based on the sensor signal separation-spectrum reconstruction data and the sensor signal timing recovery-channel alignment data, perform disturbance fluctuation characteristics analysis of the sensor signal under the corresponding impact state, and generate disturbance fluctuation characteristic data of the sensor signal under the corresponding impact state; Step S344: Based on the disturbance fluctuation characteristic data of the sensor signal under the corresponding impact state, perform multi-channel collaborative buffering processing on the sensor signal timing recovery-channel alignment data to generate multi-channel collaborative buffered data of the sensor signal.

5. The sensor signal buffering method for high-impact scenarios according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform time-domain and waveform decomposition of the sensor buffer signal based on the multi-channel collaborative buffer data of the sensor signal to generate time-domain-waveform data of the sensor buffer signal; Step S42: Analyze the peak attenuation and oscillation period of the sensor signal based on the time-domain waveform data of the sensor buffer signal, and generate sensor signal peak attenuation-oscillation period data; Step S43: Detect the nonlinear fluctuation characteristics of the sensor buffer signal based on the peak attenuation-oscillation period data of the sensor signal, and generate nonlinear fluctuation characteristic data of the sensor buffer signal; Step S44: Based on the nonlinear fluctuation characteristic data of the sensor buffer signal and the peak attenuation-oscillation period data of the sensor signal, perform fluctuation and attenuation analysis of the sensor buffer signal to generate sensor buffer signal fluctuation-attenuation data.

6. The sensor signal buffering method for high-impact scenarios according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Perform time-frequency domain mapping and transformation processing on the sensor signal based on the sensor buffer signal fluctuation-attenuation data to generate sensor signal time-frequency domain mapping and transformation data; Step S52: Perform sensor multi-signal and channel feature fusion processing based on the sensor signal time-frequency domain mapping conversion data to generate sensor multi-signal-channel feature fusion data; Step S53: Perform sensor signal buffer stabilization and enhancement processing based on sensor signal time-frequency domain mapping transformation data and sensor multi-signal-channel feature fusion data to generate sensor signal buffer stabilization-enhancement data.

7. A sensor signal buffering system for high-impact scenarios, characterized in that, For performing the sensor signal buffering method for high-impact scenarios as described in claim 1, the sensor signal buffering system for high-impact scenarios includes: The signal recognition module is used to acquire sensor operating data, identify sensor signal status based on sensor operating data, and generate sensor signal status data. The collision and signal coupling analysis module is used to identify sensor impacts based on sensor operating data and generate sensor impact data; and to perform sensor collision and signal coupling disturbance analysis based on sensor impact data and sensor signal status data to generate sensor collision-signal coupling disturbance data. The signal buffer module is used to perform multi-channel collaborative buffering of sensor signals based on sensor impact-signal coupling disturbance data, and generate multi-channel collaborative buffered sensor signal data. The buffer signal fluctuation and attenuation analysis module is used to perform sensor buffer signal fluctuation and attenuation analysis based on multi-channel collaborative buffer data of sensor signals, and generate sensor buffer signal fluctuation-attenuation data. The buffer signal stabilization and enhancement module is used to perform sensor signal buffer stabilization and enhancement processing based on sensor buffer signal fluctuation-attenuation data, and generate sensor signal buffer stabilization-enhancement data.

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