Method and system for informatization measurement of whole-body vibration acceleration of agricultural machine driver

Abstract

The invention discloses an informatization measurement method and system for whole-body vibration acceleration of an agricultural machine driver, and belongs to the technical field of human body comfort evaluation, and the method comprises the steps: obtaining a time domain signal of three-axis vibration acceleration at a seat of the agricultural machine driver; carrying out segmentation processing on the signal; performing fast Fourier spectrum and dynamic window processing on the segmented signal; adjusting the variable overlapping ratio of the segmented signals after the dynamic window processing; processing the signal after the overlapping ratio is adjusted based on an improved Welch method, and calculating a power spectral density function of the signal; calculating the root-mean-square value of the acceleration in each axial direction by adopting a 1 / 3 frequency multiplication method; according to the acceleration root-mean-square value of each axial direction, calculating to obtain a total weighted acceleration root-mean-square value as a final measurement result; the agricultural machinery seat vibration signal measuring device can stably operate under different environmental conditions, completes the accurate measurement task of the agricultural machinery seat vibration signal, and provides reliable data support for the vibration analysis and comfort improvement of the agricultural machinery seat.

Description

Technical Field

[0001] The present invention relates to an information-based measurement method and system for whole-body vibration acceleration of an agricultural machinery driver, belonging to the technical field of human comfort evaluation. Background Art

[0002] Most existing tractors focus solely on comprehensive functionality, neglecting driver comfort, particularly the issues caused by vibration. Prolonged exposure to vibration accelerates driver fatigue, significantly reducing working time. It also places strain on the driver's lower back, causing pain and compromising operating skills, increasing the risk of major accidents and severely impacting road safety.

[0003] Real-time acquisition of whole-body vibration acceleration information for agricultural machinery drivers is crucial for objectively evaluating their comfort and is a crucial component of a safe driving system during farm operations. The root mean square value of the combined vibration acceleration, a key parameter for evaluating driver comfort, is a crucial indicator for assessing the vibration reduction effectiveness of agricultural machinery and improving driving safety.

[0004] The current method for measuring vibration acceleration of agricultural machinery seats is usually to manually carry sensor equipment and collect data using a handheld data analyzer. After data collection, the raw data is manually imported into a computer and then processed using commercial software. The sensors used in this method are bulky and do not support continuous measurement of multiple sets of data. This not only poses a driving safety hazard during the measurement process, but also the data processing process is complex and cannot be traced, making the testing and evaluation process cumbersome. Traditional measurement methods have significant limitations in estimating the frequency spectral density of the driver's whole-body vibration signal. For example, the defects of the periodogram method (directly performing a fast Fourier transform on the signal) include:

[0005] High variance: The Fourier transform of a single-segment signal results in large estimated variance, low signal-to-noise ratio, and poor result stability.

[0006] Fixed resolution: Unable to adapt to the time-varying characteristics of non-stationary signals, resulting in a prominent contradiction between time and frequency resolution;

[0007] Severe spectrum leakage: The default rectangular window has poor sidelobe attenuation performance and is prone to introducing false frequencies under strong noise or transient interference, affecting analysis accuracy.

[0008] In addition, although the traditional Welch method reduces the variance through segmented averaging, it still has the following problems:

[0009] Fixed segment length: ignores the local stationarity differences of the signal. Long segments sacrifice time resolution, while short segments sacrifice frequency resolution, making it difficult to optimize the processing effect of non-stationary signals.

[0010] Fixed window function: The Hanning window or rectangular window is used uniformly, which cannot balance the dynamic requirements of main lobe width and side lobe attenuation.

[0011] Fixed overlap ratio: Excessive overlap increases computational redundancy, while low overlap ratio may miss transient events and reduce the integrity of the analysis results.

[0012] The above technical defects seriously restrict the accuracy and efficiency of vibration signal analysis. There is an urgent need for an information-based measurement method for the whole-body vibration acceleration of agricultural machinery drivers to solve the above problems. Summary of the Invention

[0013] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an information-based measurement method and system for the whole-body vibration acceleration of agricultural machinery drivers, which can operate stably under different environmental conditions, complete the task of accurately measuring the vibration signals of agricultural machinery seats, and provide reliable data support for vibration analysis and comfort improvement of agricultural machinery seats.

[0014] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0015] In a first aspect, the present invention provides an information-based measurement method for whole-body vibration acceleration of an agricultural machinery driver, comprising:

[0016] Obtain the time domain signal of the three-axis vibration acceleration at the agricultural machinery driver's seat;

[0017] Preprocessing the time domain signal to obtain a preprocessed signal, wherein the preprocessing includes wavelet decomposition, high frequency coefficient threshold processing, and wavelet reconstruction;

[0018] Perform segmentation processing on the preprocessed signal to obtain the segmented signal;

[0019] Performing fast Fourier spectrum and dynamic window processing on the segmented signal to obtain the segmented signal after dynamic window processing;

[0020] According to the similarity between adjacent segmented signals, the variable overlapping rate of the segmented signals after dynamic window processing is adjusted;

[0021] The signal after adjusting the overlap ratio is processed based on the improved Welch method, and the power spectral density function of the signal is calculated;

[0022] According to the power spectrum density function of the signal, the 1 / 3 frequency multiplication method is used to calculate the root mean square value of the acceleration in each axis;

[0023] According to the RMS value of acceleration in each axis, the total weighted RMS value of acceleration is calculated as the final measurement result.

[0024] Furthermore, the preprocessing of the time domain signal to obtain the preprocessed signal includes:

[0025] The original vibration signal is subjected to discrete wavelet transform and decomposed into 5 layers using the db4 wavelet basis to obtain the high-frequency detail coefficients of each layer. The high-frequency detail coefficients of the first 3 layers are subjected to soft threshold denoising, and the high-frequency detail coefficients of the 4th and 5th layers are retained.

[0026] The denoised signal is reconstructed by inverse discrete wavelet transform to obtain the preprocessed signal.

[0027] Furthermore, performing fast Fourier spectrum and dynamic window processing on the segmented signal to obtain the segmented signal after dynamic window processing includes:

[0028] Perform fast Fourier transform on the segmented signal and calculate its peak sidelobe ratio PSLR;

[0029] Dynamically select the window function based on PSLR:

[0030] When PSLR>30dB, a rectangular window is used;

[0031] When 20dB≤PSLR≤30dB, use the Kaiser window;

[0032] When PSLR < 20dB, use the Hanning window.

[0033] Furthermore, adjusting the variable overlap rate of the segmented signals after dynamic window processing according to the similarity between adjacent segmented signals includes:

[0034] Calculate the normalized correlation coefficient of adjacent signal segments;

[0035] Dynamically adjust the overlap ratio of segmented signals based on the normalized correlation coefficient:

[0036] When the normalized correlation coefficient was >0.8, the overlap rate was 30%;

[0037] When the normalized correlation coefficient was <0.5, the overlap rate was 70%;

[0038] When the normalized correlation coefficient is between 0.5 and 0.8, the overlap rate is 50%.

[0039] Furthermore, the signal processing after adjusting the overlap rate based on the improved Welch method and calculating the power spectral density function of the signal include:

[0040] Calculate the power spectral density PSD of each signal segment m (f), the calculation formula is as follows:

[0041]

[0042] Among them, X m (f) Segmented signal after dynamic window processing, where U is the window energy normalization factor;

[0043] The power spectral density function PSD of the signal is obtained by accumulating the power spectral density Welch (f), the calculation formula is as follows:

[0044]

[0045] Where K represents the total number of segments.

[0046] Furthermore, based on the power spectrum density function of the signal, the 1 / 3 frequency multiplication method is used to calculate the root mean square value of the acceleration in each axis. The formula is as follows:

[0047]

[0048] Among them, a i The center frequency is f j The root mean square value of the acceleration in the jth 1 / 3 octave band; f ui The center frequency of the 1 / 3 octave band is f i The upper limit frequency, f li The center frequency of the 1 / 3 octave band is f i The lower frequency limit.

[0049] Furthermore, the total weighted acceleration root mean square value is calculated based on the acceleration root mean square value of each axis, including:

[0050] According to the root mean square value of acceleration a in each axis i , calculate the weighted acceleration root mean square value, the formula is: a iw =w i a i ;

[0051] Where a iw It represents the weighted RMS value of the acceleration of the 1 / 3 octave band with the uniaxial center frequency as i (i=1, 2, 3...20), w i is the weighting coefficient of the i-th l / 3 octave band;

[0052] According to a iw Calculate the single-axis weighted acceleration root mean square value a w , expressed as:

[0053]

[0054] The total vibration of the weighted root mean square acceleration determined by the vibration in the orthogonal coordinate system at each test point is calculated as follows:

[0055]

[0056] Among them, a xw 、a yw 、a zw are the weighted root mean square values ​​on the corresponding orthogonal coordinate systems x, y, and z respectively;

[0057] Therefore, the total weighted acceleration root mean square value is calculated as:

[0058]

[0059] In a second aspect, the present invention provides an agricultural machinery driver's whole-body vibration acceleration information measurement system, comprising:

[0060] The hardware platform includes a piezoelectric triaxial vibration sensor, an IEPE multi-channel synchronous data acquisition card, a handheld industrial tablet, a LoRa wireless data transmission module, and an industrial computer in the control room;

[0061] A software platform, deployed on the handheld industrial tablet, comprising a data processing module, the data processing module being configured to execute the agricultural machinery driver whole-body vibration acceleration information measurement method according to any one of claims 1 to 5, and obtain a total weighted acceleration root mean square value;

[0062] The piezoelectric triaxial vibration sensor is installed on the seat of the agricultural machinery and is used to collect vibration acceleration signals in three-dimensional space in real time and convert them into electrical signals;

[0063] The IEPE multi-channel synchronous data acquisition card is connected to the piezoelectric triaxial vibration sensor via a BNC interface to synchronously sample and cache the converted electrical signals;

[0064] The handheld industrial tablet is used to receive the electrical signal transmitted by the IEPE multi-channel synchronous data acquisition card and process it to obtain the measurement result;

[0065] The LoRa wireless data transmission module is used to wirelessly transmit the processed measurement results to the industrial computer in the main control room.

[0066] Furthermore, the software platform also includes: a serial communication module, a data display module and a result processing module; wherein:

[0067] The serial communication module is used to realize data exchange between the data processing module and the sensor data acquisition terminal;

[0068] The data display module is used to display the total weighted acceleration root mean square value calculated by the data processing module;

[0069] The result processing module is used to store the measurement results in the industrial tablet and transmit the measurement results to the industrial computer in the main control room through the LoRa wireless data transmission module for archiving and analysis.

[0070] Furthermore, the IEPE multi-channel synchronous data acquisition card is configured with a FIFO buffer larger than a set capacity, adopts a USB 3.0 bus to transmit data, and supports multi-channel synchronous sampling to avoid signal distortion.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention provides an information-based measurement method and system for the whole-body vibration acceleration of agricultural machinery drivers. Through the coordination and cooperation between various hardware components, the vibration acceleration signal of the agricultural machinery seat in three-dimensional space is collected and the total weighted acceleration root mean square value is calculated as the final measurement result. The method can operate stably under different environmental conditions and complete the task of accurately measuring the vibration signal of the agricultural machinery seat, providing reliable data support for vibration analysis and comfort improvement of agricultural machinery seats. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a diagram of the hardware platform design structure provided by an embodiment of the present invention;

[0074] Figure 2 is a schematic diagram of system hardware and data transmission provided by an embodiment of the present invention;

[0075] Figure 3 This is a system software framework diagram provided by an embodiment of the present invention;

[0076] Figure 4 is a functional diagram of the software system provided by an embodiment of the present invention;

[0077] Figure 5 This is a flow chart of a data processing algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0079] Example 1: This example introduces a method for measuring the whole-body vibration acceleration of an agricultural machinery driver in an information-based manner, including:

[0080] Obtain the time domain signal of the three-axis vibration acceleration at the agricultural machinery driver's seat;

[0081] Preprocessing the time domain signal to obtain a preprocessed signal, wherein the preprocessing includes wavelet decomposition, high frequency coefficient threshold processing, and wavelet reconstruction;

[0082] Perform segmentation processing on the preprocessed signal to obtain the segmented signal;

[0083] Performing fast Fourier spectrum and dynamic window processing on the segmented signal to obtain the segmented signal after dynamic window processing;

[0084] According to the similarity between adjacent segmented signals, the variable overlapping rate of the segmented signals after dynamic window processing is adjusted;

[0085] The signal after adjusting the overlap ratio is processed based on the improved Welch method, and the power spectral density function of the signal is calculated;

[0086] According to the power spectrum density function of the signal, the 1 / 3 frequency multiplication method is used to calculate the root mean square value of the acceleration in each axis;

[0087] According to the RMS value of acceleration in each axis, the total weighted RMS value of acceleration is calculated as the final measurement result.

[0088] The application process of the agricultural machinery driver whole-body vibration acceleration information measurement method provided in this embodiment specifically involves the following steps:

[0089] (1) Signal preprocessing;

[0090] 1. Wavelet decomposition;

[0091] Assume that the original signal is x(n), and use discrete wavelet transform (DWT) for multi-scale decomposition, select db4 wavelet basis, and the number of decomposition layers is J = 5. After decomposition, the coefficients of each layer are obtained:

[0092] Approximation coefficient: A J (Jth layer low-frequency component);

[0093] Detail coefficients: D1, D2, …, D J (high-frequency components of each layer);

[0094] Follow the decomposition formula:

[0095]

[0096] Among them, φ j,k (n) is the scaling function (low-frequency basis); ψ j,k (n) is the wavelet function (high-frequency basis); j is the number of decomposition layers, and k is the translation parameter.

[0097] 2. High frequency coefficient threshold processing;

[0098] The high-frequency detail coefficients D1, D2, and D3 are soft-thresholded to suppress noise; while higher layers (such as D4 and D5) usually contain less noise, and their original values ​​are retained to maintain the main structure of the signal.

[0099] The soft threshold function is:

[0100] D′ J [k]=sign(D j [k])·max(0,|D j [k]|-λ j )(3);

[0101] Where N is the signal length; the universal threshold Noise standard deviation of the jth layer (calculated by median estimation method).

[0102] 3. Wavelet reconstruction;

[0103] The processed coefficients {A5, D5′, D4′, D3′, D2′, D1′} are reconstructed through inverse discrete wavelet transform (IDWT), that is, through layer-by-layer upsampling and filtering.

[0104] First, reconstruct the fifth layer signal and input the approximate coefficient A5 and detail coefficient D5′; use the reconstruction low-pass filter g and high-pass filter h to upsample and filter. The reconstruction formula is:

[0105] A4=Upsample(A5)*g+Upsample(D5′)*h(4);

[0106] Furthermore, for each layer j = 5, 4, ..., 1, we reconstruct it layer by layer and perform recursive calculation: A j-1 =Upsample(A j )*g+Upsample(D j ′)*h(5);

[0107] The approximate coefficient A0 of the 0th layer is obtained, which is the reconstructed signal after denoising:

[0108] x denoised (n) = A0[n](6);

[0109] (2) Adaptive window function selection;

[0110] 1. Segment processing;

[0111] For the preprocessed signal x denoised (n) is segmented into M segments, each segment is N in length, then the signal of segment m is:

[0112] x m (n) = x denoised (n)(n=0,1,...,N-1)(7);

[0113] 2. Dynamic window selection based on PSLR;

[0114] First, for the mth segment signal x m (n) Perform Fast Fourier Transform (FFT) spectrum operations:

[0115] x m (f) = FFT[x m (n)](f=0,1,...,N-1)(8);

[0116] Furthermore, the signal peak-to-sidelobe ratio (PSLR) is calculated;

[0117]

[0118] Among them, the main lobe area is the frequency f with the maximum peak of the spectrum amplitude peak Centered, expanding left and right (Main lobe width); the side lobe area is all frequency points outside the main lobe area.

[0119] According to PSLR m Dynamically select the optimal window function, when PSLR m >30dB, select rectangular window w rect (n) Reduce spectrum leakage and prioritize frequency resolution; when 20dB≤PSLR m When ≤30dB, select Caesar window w kaiser (n, β), where parameter β = 3 + 0.1 × PSLR m , to balance the main lobe width and side lobe attenuation; when PSLR m <20dB, select Hanning window w hann (n), at this time the spectrum leakage is serious and strong sidelobe suppression is required.

[0120] Through the above operations, the frequency domain segmented signal x m (f) The segmented signal X after dynamic window processing is further obtained m (f).

[0121] (3) variable overlap ratio adjustment;

[0122] For adjacent segments x m (n) and x m+1 (n), in order to judge the similarity between segments, the normalized correlation coefficient is calculated:

[0123]

[0124] If the adjacent segment x m (n) and x m+1 (n) is high similarity, that is, ρ m,m+1 >0.8, then set the overlap ratio Rm = 30% to reduce redundancy;

[0125] If the adjacent segment x m (n) and x m+1 (n) is low similarity, that is, ρ m,m+1 <0.5, then set the overlap ratio R m =70% to increase smoothness;

[0126] If the adjacent segment x m (n) and x m+1 (n) is medium similarity, i.e. 0.5≤ρ m,m+1 When ≤0.8, set the overlap ratio R m =50%.

[0127] (4) Improved Welch method to estimate power spectral density;

[0128] First, set the current segment starting position p0 = 0, the total number of segments K = 0, and the cumulative power spectrum PSD sum (f) = 0;

[0129] For the segmented signal X after the above dynamic window processing m (f), calculate the power spectrum of each signal segment:

[0130]

[0131] in, is the window energy normalization factor.

[0132] Furthermore, the cumulative power spectrum is calculated, namely:

[0133]

[0134] Calculate the starting position of the next segment:

[0135]

[0136] According to formula (10), m,m+1 To update R m+1 .

[0137] Finally, the total signal power spectrum density value is calculated:

[0138]

[0139] (5) Calculation of the root mean square value of the combined vibration acceleration;

[0140] 1. Use the 1 / 3 frequency octave method to divide the frequency band;

[0141] The 1 / 3 octave analysis method is to divide the experimental analysis frequency band into several frequency bands as shown in formula 15, and calculate the center frequency of each frequency band using its upper and lower frequencies as shown in formula 16.

[0142]

[0143] Where:

[0144] f u The upper limit frequency of the 1 / 3 octave band; f l is the lower limit frequency of the 1 / 3 octave band; f c is the center frequency of the 1 / 3 octave band;

[0145] 2. Calculate the RMS value of each axial acceleration;

[0146] The power spectral density function PSD calculated by the Welch method Welch , integrate in each 1 / 3 octave band, and take the root of the result to obtain the RMS value component a of each acceleration in the 1 / 3 octave band i ,Right now:

[0147]

[0148] Among them, a i ——The center frequency is f j The acceleration root mean square spectrum value of the jth 1 / 3 octave band; f ui 、f li ——The center frequency of the 1 / 3 octave band is f i Upper and lower frequency limits;

[0149] 3. Calculate the root mean square value of the weighted acceleration in each axis;

[0150] According to the calculated uniaxial acceleration root mean square spectrum value a of each 1 / 3 octave band i , calculate the weighted RMS value of acceleration, that is: a iw =w i a i .

[0151] Where a iw w represents the weighted RMS value of the acceleration in the 1 / 3 octave band with the uniaxial center frequency as i (i=1, 2, 3...20); i is the weighting coefficient of the i-th l / 3 octave band.

[0152] Therefore, the uniaxial weighted acceleration root mean square value a w It can be expressed as:

[0153]

[0154] 4. Calculate the total axial weighted acceleration;

[0155] The total vibration of the weighted root mean square acceleration determined by the vibration in the orthogonal coordinate system at each test point is calculated as follows:

[0156]

[0157] Among them, a xw 、a yw 、a zw are the weighted root mean square accelerations on the corresponding orthogonal coordinate systems x, y, and z, respectively. In the case of measuring the driver's human comfort, k x =1.4, k y =1.4, k z =1.

[0158] Therefore, the total weighted rms acceleration is:

[0159]

[0160] Example 2: This embodiment provides an agricultural machinery driver whole-body vibration acceleration information measurement system, comprising:

[0161] The hardware platform includes a piezoelectric triaxial vibration sensor, an IEPE multi-channel synchronous data acquisition card, a handheld industrial tablet, a LoRa wireless data transmission module, and an industrial computer in the control room;

[0162] A software platform, deployed on the handheld industrial tablet, comprising a data processing module, a serial communication module, a data display module, and a result processing module; the data processing module is used to execute the agricultural machinery driver whole-body vibration acceleration information measurement method according to any one of claims 1 to 5 to obtain a total weighted acceleration root mean square value;

[0163] The piezoelectric triaxial vibration sensor is installed on the seat of the agricultural machinery and is used to collect vibration acceleration signals in three-dimensional space in real time and convert them into electrical signals;

[0164] The IEPE multi-channel synchronous data acquisition card is connected to the piezoelectric triaxial vibration sensor via a BNC interface to synchronously sample and cache the converted electrical signals;

[0165] The handheld industrial tablet is used to receive the electrical signal transmitted by the IEPE multi-channel synchronous data acquisition card and process it to obtain the measurement result;

[0166] The LoRa wireless data transmission module is used to wirelessly transmit the obtained measurement results to the industrial computer in the main control room;

[0167] The serial communication module is used to realize data exchange between the data processing module and the sensor data acquisition terminal;

[0168] The data display module is used to display the total weighted acceleration root mean square value calculated by the data processing module;

[0169] The result processing module is used to store the measurement results in the industrial tablet and transmit the measurement results to the industrial computer in the main control room through the LoRa wireless data transmission module for archiving and analysis.

[0170] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0171] The following describes the contents involved in the above embodiment in conjunction with a preferred embodiment.

[0172] The whole-body vibration acceleration system for agricultural machinery drivers mainly consists of a piezoelectric triaxial vibration sensor, a multi-channel data acquisition card, a wireless LoRa module, a handheld industrial tablet, and a PC for the main control console. The hardware platform design structure is as follows: Figure 1 shown.

[0173] (1) Three-axis seat cushion piezoelectric accelerometer sensor: Installed in a designated location on the seat of agricultural machinery, it is used to sense the vibration acceleration information of the seat in three-dimensional space in real time and convert the vibration signal into an electrical signal. When the seat is subjected to external vibration or compression, the piezoelectric crystal deforms and releases the corresponding charge. The sensor is equipped with a special processing circuit that can accurately convert the charge generated by the piezoelectric crystal into a high-level signal, and then output a low-impedance voltage signal to ensure the stability and anti-interference ability of the signal during transmission.

[0174] (2) IEPE multi-channel synchronous data acquisition card: It is connected to the accelerometer sensor through three BNC connectors and is used to receive the analog voltage signal output by the sensor. The acquisition card has multi-channel input, high sampling rate, low noise and synchronous sampling functions. It can accurately acquire vibration signals in multiple directions at the same time and ensure the synchronization of signal sampling in each channel, effectively avoiding signal distortion. The IEPE multi-channel synchronous data acquisition card is equipped with a 191K ultra-large FIFO (first-in-first-out) cache, which can temporarily store a large amount of data during the data acquisition process, effectively preventing data loss caused by instantaneous congestion in data transmission, and fully ensuring the integrity of the signal. At the same time, the acquisition card adopts USB3.0 high-speed bus transmission mode to losslessly transmit the converted digital signal to the industrial tablet for real-time processing, meeting the system's strict requirements for data transmission speed and accuracy.

[0175] (3) Handheld industrial tablet: This not only serves as the system's control terminal and data processing center, but also directly powers the entire system through the USB port, eliminating the need for an additional power bank. This power supply method not only simplifies the system structure, reduces system complexity and failure rate, but also ensures system portability and safety, while meeting the system's needs for long-term continuous operation.

[0176] (4) LoRa wireless data transmission module: To ensure data traceability, the system is also equipped with a wireless LoRa module that transmits signal data to an industrial PC via the RS232 protocol. After receiving the vibration signal data from the agricultural machinery seat, the industrial PC organizes, analyzes, and stores it for long-term storage, allowing for further research and evaluation of the vibration performance of the agricultural machinery seat.

[0177] The coordination between the hardware components of this system enables stable operation under different environmental conditions, and completes the task of accurately measuring the vibration signals of agricultural machinery seats, providing reliable data support for vibration analysis and comfort improvement of agricultural machinery seats. The schematic diagram of the transmission between the hardware and data of the entire system is shown in the figure. Figure 2 As shown;

[0178] like Figure 4 This is a functional diagram of the software system. The software system specifically includes:

[0179] (1)Serial communication module

[0180] This module, deployed in a handheld industrial tablet, facilitates data exchange between the host computer and the sensor data acquisition terminal. Its workflow is as follows: First, configure the operating mode and set serial port communication parameters to ensure a stable communication connection between the host computer and the data acquisition terminal. Once the connection is established, it receives vibration sensor data collected by the IEPE data acquisition card and implements encoded transmission of the vibration signal data. This module is essential for the normal operation of the system, ensuring stable communication between the host computer and the sensor data acquisition terminal.

[0181] (2) Data processing module

[0182] As the core module of this system, this module, also deployed in a handheld industrial tablet, is primarily used to process data collected by the sensor data acquisition terminal. The processing flow is as follows: First, because the input signal from the acquisition terminal is a stream of acceleration data rather than a time-domain signal, a two-dimensional curve fitting is performed on the data. Next, considering the potential for various noises to be mixed into the data during acquisition, filtering and denoising are performed before processing and analysis to improve the reliability and authenticity of the data. Finally, a weighted calculation is performed to obtain the RMS acceleration value for each axis.

[0183] (3) Data display module

[0184] This module, deployed on a handheld industrial tablet, displays the RMS acceleration values ​​of each axis, calculated by the data processing module, along with the further calculated RMS value of the combined acceleration. This module dynamically displays the data from each measurement, allowing users to intuitively view and analyze measurement data, ensuring transparency and accuracy of the measurement process.

[0185] (4) Result processing module

[0186] Deployed in a handheld industrial tablet, the module displays the final measurement results (the combined RMS value of acceleration) and stores them on the tablet. Furthermore, the module supports transmitting measurement results to an industrial PC via a LoRa module, enabling users to archive and further analyze the results.

[0187] At the same time, the above modules can also be further summarized as the host computer end and the sensor data acquisition end to realize the interaction between the physical layer and the application layer, such as Figure 3 The figure shows the system software framework diagram; among them, the host computer end includes a serial communication module; the sensor data acquisition end includes a data processing module, a data display module and a result processing module.

[0188] Since the acceleration signal is a time domain signal and cannot reflect the energy information of the random vibration process, it is first converted to the frequency domain through Fourier transform and its power spectrum density (PSD) is calculated. By analyzing the energy information of each frequency point, the power spectrum of the key frequency point is determined, and then the root mean square value of the combined vibration acceleration at the agricultural machinery seat is calculated. The data processing algorithm flow chart is shown in the figure below. Figure 5 .

[0189] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for measuring the whole-body vibration acceleration of an agricultural machinery driver, characterized in that: include: Obtain the time domain signal of the three-axis vibration acceleration at the agricultural machinery driver's seat; Preprocessing the time domain signal to obtain a preprocessed signal, wherein the preprocessing includes wavelet decomposition, high frequency coefficient threshold processing, and wavelet reconstruction; Perform segmentation processing on the preprocessed signal to obtain the segmented signal; Performing fast Fourier spectrum and dynamic window processing on the segmented signal to obtain the segmented signal after dynamic window processing; According to the similarity between adjacent segmented signals, the variable overlapping rate of the segmented signals after dynamic window processing is adjusted; The signal after adjusting the overlap ratio is processed based on the improved Welch method, and the power spectral density function of the signal is calculated; According to the power spectrum density function of the signal, the 1 / 3 frequency multiplication method is used to calculate the root mean square value of the acceleration in each axis; According to the RMS value of acceleration in each axis, the total weighted RMS value of acceleration is calculated as the final measurement result.

2. The method for measuring the whole-body vibration acceleration of an agricultural machinery driver according to claim 1, characterized in that: The preprocessing of the time domain signal to obtain the preprocessed signal includes: The original vibration signal is subjected to discrete wavelet transform and decomposed into 5 layers using the db4 wavelet basis to obtain the high-frequency detail coefficients of each layer. The high-frequency detail coefficients of the first 3 layers are subjected to soft threshold denoising, and the high-frequency detail coefficients of the 4th and 5th layers are retained. The denoised signal is reconstructed by inverse discrete wavelet transform to obtain the preprocessed signal.

3. The method for measuring the whole-body vibration acceleration of an agricultural machinery driver according to claim 1, characterized in that: The performing fast Fourier spectrum and dynamic window processing on the segmented signal to obtain the segmented signal after dynamic window processing includes: Perform fast Fourier transform on the segmented signal and calculate its peak sidelobe ratio PSLR; Dynamically select the window function based on PSLR: When PSLR>30dB, a rectangular window is used; When 20dB≤PSLR≤30dB, use the Kaiser window; When PSLR < 20dB, use the Hanning window.

4. The method for measuring the whole-body vibration acceleration of an agricultural machinery driver according to claim 1, characterized in that: The step of adjusting the variable overlap rate of the segmented signals after dynamic window processing according to the similarity between adjacent segmented signals includes: Calculate the normalized correlation coefficient of adjacent signal segments; Dynamically adjust the overlap ratio of segmented signals based on the normalized correlation coefficient: When the normalized correlation coefficient was >0.8, the overlap rate was 30%; When the normalized correlation coefficient was <0.5, the overlap rate was 70%; When the normalized correlation coefficient is between 0.5 and 0.8, the overlap rate is 50%.

5. The method for measuring the whole-body vibration acceleration of an agricultural machine driver according to claim 1, characterized in that: The signal processing after adjusting the overlap rate based on the improved Welch method and calculating the power spectrum density function of the signal include: Calculate the power spectral density PSD of each signal segment m (f), the calculation formula is as follows: Among them, X m (f) Segmented signal after dynamic window processing, where U is the window energy normalization factor; The power spectral density function PSD of the signal is obtained by accumulating the power spectral density Welch (f), the calculation formula is as follows: Where K represents the total number of segments.

6. The method for measuring the whole-body vibration acceleration of an agricultural machine driver according to claim 5, characterized in that: According to the power spectrum density function of the signal, the 1 / 3 frequency multiplication method is used to calculate the root mean square value of the acceleration in each axis. The formula is as follows: Among them, a i The center frequency is f j The root mean square value of the acceleration in the jth 1 / 3 octave band; f ui The center frequency of the 1 / 3 octave band is f i The upper limit frequency, f li The center frequency of the 1 / 3 octave band is f i The lower frequency limit.

7. The method for measuring the whole-body vibration acceleration of an agricultural machine driver according to claim 6, characterized in that: The total weighted acceleration root mean square value is calculated based on the acceleration root mean square value of each axis, including: According to the root mean square value of acceleration a in each axis i , calculate the weighted acceleration root mean square value, the formula is: a iw =w i a i ; Where a iw It represents the weighted RMS value of the acceleration of the 1 / 3 octave band with the uniaxial center frequency as i (i=1, 2, 3...20), w i is the weighting coefficient of the i-th l / 3 octave band; According to a iw Calculate the single-axis weighted acceleration root mean square value a w , expressed as: The total vibration of the weighted root mean square acceleration determined by the vibration in the orthogonal coordinate system at each test point is calculated as follows: Among them, a xw 、a yw 、a zw are the weighted root mean square values ​​on the corresponding orthogonal coordinate systems x, y, and z respectively; Therefore, the total weighted acceleration root mean square value is calculated as:

8. A whole-body vibration acceleration information measurement system for agricultural machinery drivers, characterized in that: include: The hardware platform includes a piezoelectric triaxial vibration sensor, an IEPE multi-channel synchronous data acquisition card, a handheld industrial tablet, a LoRa wireless data transmission module, and an industrial computer in the control room; A software platform, deployed on the handheld industrial tablet, comprising a data processing module, the data processing module being configured to execute the agricultural machinery driver whole-body vibration acceleration information measurement method according to any one of claims 1 to 5, and obtain a total weighted acceleration root mean square value; The piezoelectric triaxial vibration sensor is installed on the seat of the agricultural machinery and is used to collect vibration acceleration signals in three-dimensional space in real time and convert them into electrical signals; The IEPE multi-channel synchronous data acquisition card is connected to the piezoelectric triaxial vibration sensor via a BNC interface to synchronously sample and cache the converted electrical signals; The handheld industrial tablet is used to receive the electrical signal transmitted by the IEPE multi-channel synchronous data acquisition card and process it to obtain the measurement result; The LoRa wireless data transmission module is used to wirelessly transmit the processed measurement results to the industrial computer in the main control room.

9. The agricultural machinery driver whole body vibration acceleration information measurement system according to claim 8, characterized in that: The software platform also includes: a serial communication module, a data display module and a result processing module; wherein: The serial communication module is used to realize data exchange between the data processing module and the sensor data acquisition terminal; The data display module is used to display the total weighted acceleration root mean square value calculated by the data processing module; The result processing module is used to store the measurement results in the industrial tablet and transmit the measurement results to the industrial computer in the main control room through the LoRa wireless data transmission module for archiving and analysis.

10. The agricultural machinery driver whole body vibration acceleration information measurement system according to claim 6, characterized in that: The IEPE multi-channel synchronous data acquisition card is configured with a FIFO buffer larger than a set capacity, adopts a USB 3.0 bus to transmit data, and supports multi-channel synchronous sampling to avoid signal distortion.

Images