Pulse signal wireless transmission and calibration method based on cloud edge collaboration

Abstract

The application discloses a pulse signal wireless transmission and calibration method based on cloud edge cooperation and relates to the technical field of signal transmission.The edge node collects reference noise in a transmission environment, generates estimated noise through an adaptive filter, denoises a received noisy pulse signal, and performs amplitude distortion detection and preliminary correction;the edge node exchanges time stamps through a self-organizing network to achieve preliminary time synchronization, combines an absolute time signal in the cloud, fine-tunes a local clock by adopting a PI control algorithm, and realizes time unification of the whole network;the edge node detects actual characteristic points of the pulse signal through a sliding window, aligns the actual characteristic points with standard characteristic points, adjusts window parameters, and performs local calibration in combination with local initial calibration parameters;the cloud issues initial calibration parameters to the edge node, the edge node performs secondary calibration and uploads local optimization parameters, the cloud performs weighted fusion to generate global optimal parameters, and iterative optimization is performed until calibration error is stable, so that the real-time performance and accuracy of the signal are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal transmission, in particular to a pulse signal wireless transmission and calibration method based on cloud-edge collaboration. BACKGROUND

[0002] In the battery management system (BMS) and motor control system of new energy vehicles, the wireless transmission and calibration technology of pulse signals is crucial for real-time monitoring and control. Currently, the mainstream pulse signal transmission methods mainly fall into two categories: cloud centralized processing and edge node independent processing. The cloud centralized processing method uses remote servers to denoise, synchronize and calibrate signals, which can take advantage of the powerful computing power of the cloud. However, due to the long data transmission link, it results in high delay and poor real-time performance, making it difficult to meet the needs of high-speed dynamic detection of new energy vehicles. The edge node independent processing method relies on local algorithms for signal processing, which has fast response speed, but is limited by the lack of collaboration capabilities between nodes, making it difficult to achieve high-precision time synchronization and global parameter optimization, resulting in large signal calibration errors.

[0003] In the prior art, the noise elimination of edge nodes usually uses a fixed parameter filter, which cannot adapt to the dynamic noise changes in the complex electromagnetic environment of new energy vehicles, resulting in unstable denoising effect and affecting the accuracy of pulse signals. The time synchronization between nodes mainly relies on a single clock synchronization protocol such as NTP or PTP. In an environment where wireless signals are easily disturbed, the synchronization accuracy is difficult to guarantee, which further affects the timing consistency of signal transmission. These problems are particularly prominent in the high-frequency pulse signal detection scenario of new energy vehicles, which may lead to incorrect battery state judgment or motor control misalignment, affecting the safety and reliability of the system.

[0004] In addition, existing calibration methods usually use centralized parameter distribution or local optimization strategies, lacking an iterative optimization mechanism for cloud-edge collaboration. The parameters distributed by the cloud are based on global statistics, which are difficult to adapt to the local characteristics of different edge nodes. The local optimization of edge nodes lacks a global perspective, which easily falls into local optimization. This fragmented processing approach results in low calibration efficiency and slow error convergence, which cannot meet the needs of high-precision, low-delay pulse signal transmission in new energy vehicle detection scenarios. SUMMARY

[0005] (I) Technical problems solved

[0006] To address the shortcomings of the prior art, the present application provides a pulse signal wireless transmission and calibration method based on cloud-edge collaboration, which solves the problems of large pulse signal transmission noise, low synchronization accuracy and poor calibration efficiency in the complex electromagnetic environment of new energy vehicles through adaptive filter denoising, dynamic time synchronization and cloud-edge collaborative iterative optimization, significantly improving the real-time performance and accuracy of signals.

[0007] (II) Technical solution

[0008] To achieve the above object, the present application is implemented by the following technical solutions: a pulse signal wireless transmission and calibration method based on cloud-edge collaboration, comprising:

[0009] The edge node collects reference noise in the transmission environment, generates estimated noise through an adaptive filter, denoises the received noisy pulse signal, and performs amplitude distortion detection and preliminary correction;

[0010] The edge node exchanges time stamps through a self-organizing network to achieve preliminary time synchronization, combines with the cloud absolute time signal, and uses a PI control algorithm to fine-tune the local clock to achieve network time unification;

[0011] The edge node dynamically detects the actual feature points of the pulse signal through a sliding window, aligns with the pre-stored standard feature points, adjusts the window parameters, and performs local calibration combined with the local initial calibration parameters;

[0012] The cloud end issues initial calibration parameters to the edge node, the edge node performs secondary calibration and uploads local optimization parameters, the cloud end weightedly fuses to generate global optimal parameters, and iteratively optimizes until the calibration error is stable.

[0013] Further, the edge node receives the noisy pulse signal through the wireless receiving module, and simultaneously collects the reference noise which is homologous to the noise in the pulse signal; the initial parameters of the adaptive filter are randomly set within a preset range, and the reference noise is filtered to generate estimated noise; the actual noise in the noisy pulse signal is extracted, the mean square error of the estimated noise and the actual noise is calculated, and the filter parameters are adjusted by gradient descent method until the error is less than a preset error threshold.

[0014] Further, after time alignment of the noisy pulse signal and the estimated noise, point-by-point subtraction operation is performed to obtain the denoised pulse signal; the amplitude distortion region of the denoised pulse signal is detected, and linear correction algorithm is used for preliminary amplitude correction to make the signal amplitude fall within the amplitude range of the standard pulse.

[0015] Further, each edge node records its local time through a local timing module; through a self-organizing network, a time stamp data packet containing node ID and local time is broadcasted to all adjacent nodes in the network at a fixed period; the time stamp data packet of the adjacent node is received, the preliminary deviation from the local time is calculated, and the transmission time consumption is deducted to serve as the correction basis; the deviation values of multiple adjacent nodes are collected, the average value after removing outliers is taken as the local clock correction amount, the crystal oscillator frequency is adjusted to correct the clock in real time; the time stamp exchange and clock correction are repeatedly performed until the time difference between adjacent nodes is less than a preset time difference threshold.

[0016] Further, the cloud server acts as a time master node, and broadcasts a synchronization signal containing an absolute time and a broadcast time mark to all edge nodes at a fixed period.

[0017] Further, the edge node pre-stores characteristic parameters of a standard pulse waveform, including a peak position, a rising edge start point, and a falling edge end point; an initial sliding window size and a starting position are set according to a standard pulse duration and a sampling rate; the actual pulse characteristic points are detected by moving the sliding window, the position deviation from the standard characteristic points is calculated, and the characteristic points are aligned by adjusting the window position.

[0018] Further, if the actual pulse length deviates from the standard length by more than a preset length threshold, the window size is dynamically adjusted to ensure that the waveform is completely contained; based on the aligned characteristic points, the local initial calibration parameters are called to perform phase and timing compensation, and the phase deviation and time mark of the signal are corrected.

[0019] Further, the cloud server distributes initial calibration parameters to each edge node through an encrypted channel, and the parameters include a phase calibration coefficient, a timing delay correction value, and an amplitude compensation factor; the edge node uses the initial calibration parameters to perform secondary calibration on the locally calibrated pulse signal, and calculates the root mean square error of the calibrated signal and the standard template.

[0020] Further, the edge node adjusts the calibration parameters locally based on the mean square error through a particle swarm optimization algorithm, obtains local optimization parameters and corresponding error values, and uploads them to the cloud server; the cloud server assigns weights according to the amount of node data and reliability, fuses the local optimization parameters to generate global optimal parameters by using a weighted least squares method; the global optimal parameters are broadcast to the edge nodes to replace the local parameters, and the iterative optimization is repeated until the global calibration error change is less than a preset change threshold.

[0021] An electronic device includes a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor implements a cloud-edge collaborative pulse signal wireless transmission and calibration method when executing the computer program.

[0022] A computer readable storage medium has a computer program stored thereon, and the computer program implements a cloud-edge collaborative pulse signal wireless transmission and calibration method when executed.

[0023] (Three) beneficial effects

[0024] The present application provides a cloud-edge collaborative pulse signal wireless transmission and calibration method, which has the following beneficial effects:

[0025] (1) The adaptive filter generates an estimated noise and performs time alignment and point-by-point subtraction denoising, effectively eliminating the homologous noise in the pulse signal, significantly improving the signal-to-noise ratio of the signal, reducing signal distortion through amplitude distortion detection and preliminary correction, ensuring the initial quality of the pulse signal, laying a reliable foundation for subsequent time synchronization and calibration, and improving the accuracy and stability of the overall system.

[0026] (2) Through the cooperation of timestamp exchange of self-organizing network and absolute time synchronization signal of cloud, high-precision time synchronization between edge nodes is realized, and the local clock is fine-tuned using PI control algorithm, so that the time deviation of the whole network is controlled within ±5μs, the timing consistency of pulse signal transmission is significantly improved, the anti-interference ability of the system is enhanced, and a reliable time reference is provided for subsequent signal calibration, ensuring the stability and synchronization accuracy of the whole cloud-edge collaborative network.

[0027] (3) The actual pulse feature points are dynamically detected by sliding window and aligned with the pre-stored standard parameters, the accurate phase and timing compensation of the pulse signal is realized, the window position and size can be adaptively adjusted, the feature point deviation is effectively eliminated, the accuracy and consistency of the pulse waveform are ensured, and the phase and timing accuracy of the signal is further optimized combined with the local initial calibration parameters, laying a reliable foundation for subsequent global calibration, significantly improving the overall calibration efficiency and signal quality of the system.

[0028] (4) The initial calibration parameters are issued by the cloud and the edge nodes are guided for secondary calibration, the local parameter optimization is realized combined with particle swarm optimization algorithm, the calibration accuracy is significantly improved, the global optimal parameters are generated by the cloud by weighting and fusing the local optimization results of each node, and the calibration error is quickly converged to a stable state through iterative optimization, the advantages of cloud-edge collaboration are fully utilized, high-precision and adaptive distributed calibration is realized, the bandwidth occupation is reduced, and the efficiency and scalability of the system are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0029] Fig. 1 The figure is a step schematic diagram of the pulse signal wireless transmission and calibration method based on cloud-edge collaboration of the application.

[0030] Fig. 2 The figure is a preliminary correction process schematic diagram of the adaptive denoising of the application.

[0031] Fig. 3 The figure is a local calibration process schematic diagram of the application. DETAILED DESCRIPTION

[0032] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0033] Please refer to Figs. 1-3 The present application provides a cloud edge collaborative pulse signal wireless transmission and calibration method, comprising the following steps:

[0034] Step one: the edge node collects the reference noise in the transmission environment, generates the estimated noise through the adaptive filter, denoises the received noisy pulse signal, and performs amplitude distortion detection and preliminary correction;

[0035] The step one comprises the following contents:

[0036] Step 101: the edge node receives the noisy pulse signal transmitted externally through the built-in wireless receiving module, for example, receives the external pulse signal through the radio frequency receiving circuit, and at the same time, starts the noise collection module of the same node, which is in the same transmission environment as the wireless receiving module, such as sharing the antenna or adjacent sampling channels, and collects the reference noise in the transmission environment in the same period of receiving the pulse signal; wherein the reference noise and the noise mixed in the pulse signal are homologous noise, that is, they come from the same noise source and have the same frequency characteristics and amplitude distribution characteristics;

[0037] Step 102: the edge device activates the built-in adjustable adaptive filter, such as the adaptive filter based on LMS algorithm, the initial parameters of the filter are randomly selected within the preset range, and the preset range is set based on the frequency spectrum characteristics of the common noise in the pulse signal transmission scene, for example, the initial range of filter order is set to 8-32 orders, and the initial range of filter coefficient is set to [-0.5, 0.5];

[0038] Step 103: using the adjustable adaptive filter to filter the collected reference noise, outputting the estimated noise, extracting the actual noise from the received noisy pulse signal, specifically: identifying the non-pulse period in the signal through the pulse signal detection algorithm, and intercepting the sampling value of the non-pulse period as the actual noise sample; calculating the mean square error of the estimated noise and the actual noise, using the mean square error formula to calculate: Wherein, e is the mean square error, N is the number of sampling points of the noise sample, is the i-th sampling value of the actual noise, n is the i-th sampling value of the estimated noise, n is the i-th sampling value of the estimated noise.​

[0039] Step 104: Adjusting filter parameters based on mean square error e by a feedback adjustment mechanism: using gradient descent method to update filter coefficients, and adjusting step size to be , μ is a preset learning rate, with a value range of 0.01-0.1, repeatedly adjusting filter parameters until e is less than or equal to a preset error threshold, such as 0.01, and the error threshold is set according to the signal accuracy requirement;

[0040] Step 105: Time synchronization alignment of the received noisy pulse signal and the estimated noise, based on the sampling time mark of the edge node local clock, and performing point-by-point subtraction operation, subtracting the sampling value of the estimated noise at the corresponding time from each sampling value of the noisy pulse signal, to obtain the denoised pulse signal;

[0041] Step 106: Amplitude distortion detection of the denoised pulse signal, comparing the amplitude sampling value of the denoised signal with the amplitude range of the preset standard pulse (such as standard peak amplitude A±5%A), determining the amplitude distortion region, i.e. the region exceeding the amplitude range of the standard pulse, and using a linear correction algorithm to perform preliminary correction on the distortion region, with the correction formula being: wherein, is the i-th amplitude sampling value of the denoised signal, k , , is a correction coefficient, which is obtained by least square fitting the relationship between the distorted amplitude and the standard amplitude, so that the amplitude value of the corrected signal falls within the amplitude range of the standard pulse.

[0042] In use, in combination with the contents of steps 101 to 106:

[0043] The adaptive filter generates estimated noise and performs time alignment and point-by-point subtraction denoising, effectively eliminating the homologous noise in the pulse signal, significantly improving the signal-to-noise ratio of the signal, reducing signal distortion through amplitude distortion detection and preliminary correction, ensuring the initial quality of the pulse signal, and laying a reliable foundation for subsequent time synchronization and calibration, improving the accuracy and stability of the overall system.

[0044] Step two: The edge node exchanges timestamps through a self-organizing network to achieve preliminary time synchronization, combines with the cloud absolute time signal, and uses a PI control algorithm to fine-tune the local clock to achieve network-wide time unification;

[0045] The step two includes the following contents:

[0046] Step 201: Each edge node activates the built-in local timing module, such as a high-precision real-time clock (RTC) chip, starts the timing function, and records its local time in a preset format, such as a Unix timestamp, while marking the initial timing moment as the synchronization starting reference;

[0047] Step 202: The edge node forms a self-organizing network based on a preset wireless communication protocol, such as ZigBee or LoRa, and broadcasts a timestamp data packet containing its unique identifier (node ID) and current local time to all neighboring nodes in the network at a fixed period (e.g., 100 ms / time). The data packet uses CRC check to ensure transmission integrity;

[0048] Step 203: After each node receives the timestamp data packet sent by the neighboring node, it parses the local time of the neighboring node, calculates the difference between its local time and the local time of the neighboring node as the preliminary time deviation, obtains the data packet transmission time by recording the difference between the sending and receiving moments, and calculates the preliminary time deviation by deducting the data packet transmission time;

[0049] Step 204: The node collects the preliminary time deviation values of multiple (e.g., 3) neighboring nodes, removes the abnormal values that exceed the preset range (e.g., ±100 μs), and takes the arithmetic mean of the remaining deviation values as the correction amount of its clock. The local clock is real-time corrected by adjusting the frequency of the crystal oscillator of the local timing module, such as adjusting the output frequency of the crystal oscillator by 0.1 ppm for every 1 μs of deviation;

[0050] Step 205: Repeat the timestamp exchange, deviation calculation, and clock correction of steps 202 to 204. After each correction, record the time difference between nodes. When the time difference between all neighboring nodes is less than the preset time difference threshold (e.g., 10 μs) for multiple consecutive times (e.g., 5 times), it is determined that the preliminary synchronization between edge nodes is completed;

[0051] Step 206: The cloud server acts as the time master node, uses a GPS-tamed rubidium atomic clock as the clock source, and broadcasts a synchronization signal containing the absolute time and the broadcast moment mark to all edge nodes at a fixed period (e.g., 1 s / time). The synchronization signal uses encrypted transmission to prevent tampering;

[0052] Step 207: After the edge node receives the synchronization signal broadcast by the cloud, it parses the absolute time, calculates the deviation between its local current time and the absolute time, and fine-tunes the phase of the local timing module using a proportional-integral (PI) control algorithm, such as adjusting the phase by 0.1° for every 10 ns of deviation, to control the deviation between the local time and the cloud absolute time within ±5 μs, achieving time unification for the entire network.

[0053] In use, combine the contents of steps 201 to 207:

[0054] Through the synergy of timestamp exchange of self-organizing network and absolute time synchronization signal in the cloud, high-precision time synchronization between edge nodes is realized. The local clock is fine-tuned by using a PI control algorithm, the time deviation of the entire network is controlled within ±5μs, the timing consistency of pulse signal transmission is significantly improved, the anti-interference ability of the system is enhanced, a reliable time reference is provided for subsequent signal calibration, and the stability and synchronization accuracy of the entire cloud-edge collaborative network are ensured.

[0055] Step three: the edge node detects the actual feature points of the pulse signal through a sliding window, aligns with the pre-stored standard feature points, adjusts the window parameters, and performs local calibration combined with the local initial calibration parameters;

[0056] The step three includes the following contents:

[0057] Step 301: prestore the feature parameters of the standard pulse waveform in the local storage module of the edge node, including the peak position, the rising edge start point, and the falling edge end point; wherein the peak position is defined as the sampling point sequence number when the pulse amplitude reaches the maximum value, the rising edge start point is defined as the sampling point sequence number when the pulse amplitude rises from the baseline value to 10% of the standard peak value, the falling edge end point is defined as the sampling point sequence number when the pulse amplitude falls from the peak value to 10% of the standard peak value, the baseline value is the average amplitude of the pulse signal period, and the standard peak value is a preset peak value, such as 5;

[0058] Step 302: according to the expected duration of the standard pulse (such as 10μs) and the sampling rate (such as 1 sampling point per μs), set the size of the initial sliding window to the number of sampling points containing the complete pulse waveform, such as 12 sampling points, and reserve a redundancy of 2 sampling points; the initial starting position of the sliding window is set to the position where the amplitude exceeds 2 times the standard deviation of the baseline value for the first time in the pulse signal;

[0059] Step 303: move the sliding window by 1 sampling point as a step, and detect the feature points of the pulse signal in the sliding window; use a first-order difference algorithm to identify the rising edge and the falling edge, the rising edge start point is the time when the difference result changes from negative to positive and exceeds the preset rising threshold, the falling edge end point is the time when the difference result changes from positive to negative and exceeds the preset falling threshold, the peak position is located by a local extremum search algorithm, and the peak position is the sampling point corresponding to the maximum amplitude in the window; record the sampling point sequence number of the actual feature points in the window;

[0060] Step 304: Calculate the position deviation of the actual feature point from the standard feature point, such as the difference between the sample point sequence number of the actual peak position and the sample point sequence number of the standard peak position; if the absolute value of the position deviation is greater than 1 sample point, shift the sliding window by adjusting the starting sample point sequence number of the window to align the actual feature point with the standard feature point, i.e., the relative positions in the window are consistent, e.g., both are at the 6th sample point position in the window; repeat the adjustment until the absolute value of the deviation is ≤1 sample point;

[0061] Step 305: If the difference between the length from the rising edge start point to the falling edge end point of the actual pulse and the corresponding length of the standard pulse exceeds the preset length threshold, e.g., 5%, automatically adjust the window size: increase the length by 2 sample points each time when the length increases, and decrease the length by 2 sample points each time when the length decreases; after adjustment, re-execute steps 303-304 until the window completely contains the actual pulse waveform, i.e., the rising edge start point and the falling edge end point are both located within the window;

[0062] Step 306: Based on the aligned actual feature point, the edge node calls the locally stored initial calibration parameters for local calibration, including phase compensation coefficients and timing delay correction values; for phase deviation, adjust the sampling phase of the pulse signal through linear interpolation algorithm to make the actual peak phase consistent with the standard peak phase; for timing deviation, correct the time stamp of the signal according to the timestamp difference of the feature points to ensure that the timing of the pulse signal meets the standard requirements.

[0063] In use, the contents of steps 301 to 306 are combined:

[0064] By dynamically detecting the actual pulse feature points through the sliding window and aligning them with the pre-stored standard parameters, accurate phase and timing compensation of the pulse signal is achieved, the window position and size can be adjusted adaptively, the feature point deviation is effectively eliminated, the accuracy and consistency of the pulse waveform are ensured, and the phase and timing precision of the signal is further optimized by combining the local initial calibration parameters, which lays a reliable foundation for subsequent global calibration and significantly improves the overall calibration efficiency and signal quality of the system.

[0065] Step four: the cloud end issues initial calibration parameters to the edge node, the edge node performs secondary calibration and uploads local optimization parameters, the cloud end generates global optimal parameters by weighted fusion, and iteratively optimizes until the calibration error is stable.

[0066] The step four includes the following contents:

[0067] Step 401: The cloud server issues initial calibration parameters to each edge node through an encrypted communication channel, the initial calibration parameters are pre-stored in the cloud database, including phase calibration coefficients, timing delay correction values, and amplitude compensation factors, the parameter format uses JSON structure, and contains the mapping relationship between node ID and corresponding parameters;

[0068] Step 402: After the edge node receives the initial calibration parameters, it calls the local calibration module to perform secondary calibration on the locally calibrated pulse signal. The phase calibration is: , is the phase of the secondary calibrated pulse signal, is the phase of the locally calibrated pulse signal, t is the time point; timing calibration: , is the time point of the secondary calibrated pulse signal, is the time point of the locally calibrated pulse signal; amplitude calibration: , is the amplitude of the secondary calibrated pulse signal, is the amplitude of the locally calibrated pulse signal, is the amplitude compensation factor, is the phase calibration coefficient, is the timing delay correction value;

[0069] The root mean square error (RMSE) is used to calculate the deviation of the calibrated signal from the standard pulse template, and the formula is: where, M is the number of sampling points of the pulse signal, is the i th sampling value of the calibrated signal, is the corresponding sampling value of the standard template;

[0070] Step 403: Based on the root mean square error, the edge node adjusts the calibration parameters locally through the particle swarm optimization algorithm (PSO): set the particle swarm size to 20, the iteration number to 50 times, and the inertia weight to linearly decrease from 0.9 to 0.4; after each iteration, the error is recalculated, the parameter combination that minimizes the RMSE is selected as the local optimization parameter, and the local optimization parameter and the corresponding error value are packaged and uploaded to the cloud, without uploading the original pulse data to reduce bandwidth occupation;

[0071] Step 404: After the cloud receives the local optimization parameters and error values uploaded by each edge node, it assigns weights according to the following rules: the node data weight is calculated based on the number of valid parameter groups uploaded by the node in the past 1 hour, for example, the total number of groups is Z, and the number of groups of a certain node is z, then the weight is z / Z); the node reliability weight is determined based on the ratio of the historical error mean of the node to the average error of the whole network, and the weight values are normalized to sum up to 1;

[0072] Step 405: The cloud calculates the global optimal calibration parameters using the weighted least squares method: for each parameter dimension, such as the phase calibration coefficient and the timing delay correction value, the local optimization parameters of each node are multiplied by the corresponding weight and summed, and the formula is: where, Kthe total number of edge nodes, the weight of the first k node, the local optimization parameter of the node;

[0073] Step 406: The cloud end issues the globally optimal calibration parameter to all edge nodes through a broadcast mechanism, and the issued data packet contains a parameter version number, a valid time and a check code;

[0074] Step 407: After the edge node receives the globally optimal calibration parameter, the locally stored initial calibration parameter is replaced after the check code is verified to be correct; the calibration, error calculation and parameter optimization process of steps 402-406 are repeatedly executed until the change amount of the global calibration error is less than a preset change threshold, such as 0.001, and it is determined that the global calibration error is stable at a low level, and the iteration is stopped.

[0075] In use, the contents of steps 401 to 407 are combined:

[0076] The initial calibration parameter is issued by the cloud end and the edge node is guided to perform secondary calibration, the local parameter optimization is realized by combining the particle swarm optimization algorithm, the calibration accuracy is significantly improved, the cloud end generates the globally optimal parameter by weighted fusion of the local optimization results of each node, and the calibration error is quickly converged to a stable state through iterative optimization, the cloud edge collaborative advantage is fully utilized, the high-precision and self-adaptive distributed calibration is realized, the bandwidth occupation is reduced, and the efficiency and scalability of the system are ensured.

[0077] An electronic device includes a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor implements the cloud edge collaborative based pulse signal wireless transmission and calibration method when executing the computer program.

[0078] A computer readable storage medium includes a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor implements the cloud edge collaborative based pulse signal wireless transmission and calibration method when executing the computer program.

[0079] In the application, the several formulas involved are calculated by taking the values of the dimensionless formulas, and the formula is obtained by software simulation of a large amount of data to obtain a formula of the nearest real situation, and the coefficients in the formula are set by a person skilled in the art according to the actual situation.

[0080] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art can be aware that units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. Whether the functions are performed by hardware or software depends on the specific application and design constraints of the technical solutions.

[0081] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, and can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0082] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A cloud-edge collaborative based pulse signal wireless transmission and calibration method, characterized in that: Comprise: The edge node collects the reference noise in the transmission environment, generates estimated noise through an adaptive filter, denoises the received noisy pulse signal, and performs amplitude distortion detection and preliminary correction; The edge node exchanges time stamps through a self-organizing network to achieve preliminary time synchronization, combines with the absolute time signal of the cloud, and fine-tunes the local clock using a PI control algorithm to achieve network-wide time unification; The edge node dynamically detects the actual feature points of the pulse signal through a sliding window, aligns them with the pre-stored standard feature points, adjusts the window parameters, and performs local calibration combined with the local initial calibration parameters; The cloud end issues initial calibration parameters to the edge node, the edge node performs secondary calibration and uploads local optimization parameters, the cloud end weightedly fuses to generate global optimal parameters, and iteratively optimizes until the calibration error is stable.

2. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 1, characterized in that: The edge node receives the noisy pulse signal through the wireless receiving module, and collects the reference noise which is the same as the noise in the pulse signal; the initial parameters of the adaptive filter are randomly set in the preset range, and the reference noise is filtered to generate estimated noise; the actual noise in the noisy pulse signal is extracted, the mean square error of the estimated noise and the actual noise is calculated, and the filter parameters are adjusted through gradient descent method until the error is less than the preset error threshold.

3. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 2, characterized in that: After time alignment of the noisy pulse signal and the estimated noise, point-by-point subtraction operation is performed to obtain the denoised pulse signal; the amplitude distortion region of the denoised pulse signal is detected, and linear correction algorithm is used for preliminary amplitude correction to make the signal amplitude fall within the amplitude range of the standard pulse.

4. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 1, characterized in that: Each edge node records its local time through a local timing module; through a self-organizing network, it broadcasts timestamp data packets containing node ID and local time to all adjacent nodes in the network at a fixed period; it receives the timestamp data packets of adjacent nodes, calculates the preliminary deviation from its local time, and deducts the transmission time consumption as the basis for correction; it collects the deviation values of multiple adjacent nodes, removes the outliers, takes the average as the local clock correction amount, and adjusts the crystal oscillator frequency to correct the clock in real time; Repeat the timestamp exchange and clock correction until the time difference between adjacent nodes is less than the preset time difference threshold.

5. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 4, characterized in that: The cloud server as the time master node broadcasts synchronization signals containing absolute time and broadcast time mark to all edge nodes at a fixed period, and the edge nodes receive the synchronization signals broadcast by the cloud, parse the absolute time, calculate the deviation from the local current time, and fine-tune the local clock phase using the PI control algorithm.

6. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 1, characterized in that: The edge node pre-stores the feature parameters of the standard pulse waveform, including peak position, rising edge start point and falling edge end point; Set the initial sliding window size and starting position according to the standard pulse duration and sampling rate; Move the sliding window to detect the actual pulse feature points, calculate the position deviation from the standard feature points, and align the feature points by adjusting the window position; If the actual pulse length deviates from the standard length by more than the preset length threshold, dynamically adjust the window size to ensure complete waveform; based on the aligned feature points, call the local initial calibration parameters for phase and timing compensation to correct the phase deviation and time mark of the signal.

7. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 1, characterized in that: The cloud end issues initial calibration parameters to each edge node through an encrypted channel, and the parameters include phase calibration coefficients, timing delay correction values and amplitude compensation factors; the edge node uses the initial calibration parameters to perform secondary calibration on the locally calibrated pulse signals, and calculates the root mean square error of the calibrated signals and the standard template.

8. The cloud-edge collaboration based impulse signal wireless transmission and calibration method according to claim 7, characterized in that: The edge node adjusts the calibration parameters locally based on the mean square error through a particle swarm optimization algorithm, obtains local optimization parameters and corresponding error values, and uploads them to the cloud end; the cloud end assigns weights according to the node data volume and reliability, fuses the local optimization parameters to generate global optimal parameters by using a weighted least squares method; the global optimal parameters are broadcast to the edge nodes to replace the local parameters, and iterative optimization is repeated until the global calibration error change is less than a preset change threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor implements the cloud edge collaborative pulse signal wireless transmission and calibration method in any one of claims 1-8 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed to implement the cloud edge collaborative pulse signal wireless transmission and calibration method in any one of claims 1-8.

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