Vibrating type deep scarification device and method for garden operation

Abstract

The invention relates to the technical field of agricultural machinery control, in particular to a vibration type subsoiling device and method for garden operation, and the method comprises the steps: collecting vibration data of a rack and each subsoiling shovel, carrying out the noise reduction processing, and generating a vibration data time sequence; the method comprises the following steps: obtaining distribution characteristics of vibration data of each subsoiler based on a vibration data time sequence of the subsoiler, determining abnormal evaluation of each subsoiler, obtaining a vibration correlation degree of the subsoiler and a rack by combining a vibration data time sequence of the rack, converting the vibration data time sequence of the rack and each subsoiler into vibration frequency, and determining the vibration correlation degree of each subsoiler. Calculating the possibility that any vibration frequency is the inherent frequency of the rack through the vibration frequency and the vibration correlation degree of the subsoiler and the rack; obtaining all vibration frequencies of which the possibility that the vibration frequency is the inherent frequency of the rack is greater than a screening threshold value, and forming an inherent frequency range of the rack; in the actual deep scarification process, the fixed frequency range is avoided in the running process of the rack, so that the rack is prevented from resonance.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural machinery control, and in particular to a vibrating deep tillage device and method for gardening operations. Background Art

[0002] During the construction and maintenance of gardens, soil quality has a decisive influence on the growth of plants. With the passage of time and frequent human activities, garden soil is prone to compaction problems, namely, factors such as pedestrian trampling, vehicle rolling and the construction of garden facilities may cause soil particles to be closely arranged, reducing the air permeability and water permeability of the soil, which makes it difficult for plant roots to expand and breathe, thereby affecting their absorption of water and nutrients. The vibrating deep tillage device is a key equipment for deep tillage operations and has been widely used in garden operations. However, during deep tillage operations, the vibration generated by the deep tillage shovel cutting the soil of the vibrating deep tillage device may resonate with the frame, causing serious deformation of the frame and reducing the service life of the vibrating deep tillage device.

[0003] Therefore, theoretical calculation methods such as finite element analysis are usually used to determine the natural frequency of the frame in the vibratory deep tillage device. However, these calculation methods are often based on idealized models and require simplification and assumptions on the frame. In fact, the frame structure of the vibratory deep tillage device is more complex and contains a variety of nonlinear factors, which leads to inaccurate selection of the natural frequency. As a result, the vibration generated by the deep tillage shovel cutting the soil may resonate with the frame, causing serious deformation of the frame and affecting the service life of the vibratory deep tillage device. Summary of the invention

[0004] In order to solve the technical problem that the existing method for determining the natural frequency of the frame is relatively idealized, resulting in inaccurate selection of the natural frequency, the purpose of the present invention is to provide a vibrating deep loosening device and method for gardening operations, and the technical scheme adopted is as follows: Collect vibration data of the frame and each deep tiller in the vibrating deep tiller, perform noise reduction on the vibration data, and generate a time series of vibration data; Based on the time series of vibration data of the deep plowing shovel, the distribution characteristics of the vibration data of each deep plowing shovel are obtained, and the abnormal evaluation of each deep plowing shovel is determined. The vibration correlation degree between the deep plowing shovel and the frame is obtained by combining the time series of vibration data of the frame, and the time series of vibration data of the frame and each deep plowing shovel is converted into vibration frequency. The possibility of any vibration frequency being the natural frequency of the frame is calculated through the vibration frequency and the vibration correlation degree between the deep plowing shovel and the frame; A screening threshold is set to obtain all vibration frequencies whose probability of being the natural frequency of the frame is greater than the screening threshold, thereby forming a natural frequency range of the frame.

[0005] Preferably, the vibration data of the frame and each deep plowing shovel in the vibratory deep plowing device are collected, and the vibration data are subjected to noise reduction processing to generate a vibration data time series sequence, including: setting a collection frequency, collecting the vibration data of the frame and each deep plowing shovel in the vibratory deep plowing device based on the collection frequency, using median filtering to perform noise reduction processing on the vibration data, and generating a vibration data time series sequence.

[0006] Preferably, the distribution characteristics of the vibration data of each deep plowing shovel are obtained based on the time series of the vibration data of the deep plowing shovel, including: The vibration data time series of each deep tiller is equally divided into a number of sequence segments 1, and the amplitude of any sequence segment 1 is determined; The distribution characteristics of the vibration data of each deep tiller are obtained, and the corresponding calculation formula is: in, Indicates The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates The first subsoiler Sequence segment 1 The amplitude of each data point; Indicates The first subsoiler The amplitude of the sequence segment one; Indicates The first subsoiler The number of data points in a sequence segment; Indicates The first subsoiler The amplitude of the first sequence segment is The mean of the absolute values ​​of the differences in the amplitudes of all remaining sequence segments of a deep tillage shovel.

[0007] Preferably, the abnormal evaluation of each deep tiller is determined, and the corresponding calculation formula is: in, Indicates The first subsoiler Anomaly evaluation of sequence segment one; Indicates The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates that except The second The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates the number of subsoilers on a vibrating subsoiler.

[0008] Preferably, the vibration correlation degree between the subsoiler and the frame is obtained by combining the vibration data time series of the frame, including: The vibration data time series of the rack is equally divided into a plurality of sequence segments 2; The vibration correlation between the deep tiller and the frame is obtained, and the corresponding calculation formula is: in, Indicates The first subsoiler The degree of correlation between each sequence segment and the vibration of the frame; Indicates The first subsoiler Anomaly evaluation of sequence segment one; Indicates The first subsoiler Sequence segment one; Indicates the rack Sequence segment two; express and Dynamic time warping matching distance; The natural constant The exponential function of base .

[0009] Preferably, the vibration data time series of the frame and each deep tiller are converted into vibration frequencies, and the possibility of any vibration frequency being the natural frequency of the frame is calculated by the vibration frequency and the vibration correlation degree between the deep tiller and the frame, including: Perform Fourier transform on the vibration data time series of the frame and each deep tiller to obtain the vibration frequency corresponding to each vibration data; Filter the vibration frequencies that appear in the frame and each deep tiller to obtain all target vibration frequencies, obtain the vibration energy value corresponding to each target vibration frequency in the vibration data time series of the frame and each deep tiller, and calculate the possibility of resonance between the frame and the deep tiller based on any target vibration frequency; The possibility of resonance between the frame and the deep tiller is used to determine the possibility that any target vibration frequency is the natural frequency of the frame.

[0010] Preferably, the possibility of resonance between the frame and the deep tiller based on any target vibration frequency is calculated, and the corresponding calculation formula is: in, Indicates The frame and the first The possibility of resonance of a deep tillage shovel; Indicates The average value of the vibration correlation between all the sequence segments of a deep tiller and the frame; Indicates The vibration data time series of a deep tiller is The vibration energy value corresponding to the target vibration frequency; Indicates the vibration data time series of the rack. The vibration energy value corresponding to the target vibration frequency.

[0011] Preferably, the possibility of determining any target vibration frequency as the natural frequency of the frame is calculated by the following formula: in, Indicates The possibility that the target vibration frequency is the natural frequency of the frame; Indicates The frame and the first The possibility of resonance of a deep tillage shovel; Indicates The frame and the The second The possibility of resonance of a deep tillage shovel; Indicates the number of subsoilers on the vibrating subsoiler; is the normalization function.

[0012] Preferably, all vibration frequencies whose probability of being the natural frequency of the frame is greater than a screening threshold are obtained to form a natural frequency range of the frame, including: screening all target vibration frequencies whose probability of being the natural frequency of the frame is greater than a screening threshold to form the natural frequency range of the frame, and when the vibrating deep tillage device is working, avoiding the natural frequency range of the frame.

[0013] In order to solve the above-mentioned technical problems, the present application also provides a vibrating deep plowing device for gardening operations, wherein the device includes a frame and a plurality of deep plowing shovels, and stores program data. When the program data is executed, the frame and the deep plowing shovel are combined to implement the vibrating deep plowing method for gardening operations as described in any of the above items.

[0014] The present invention has the following beneficial effects: 1. The vibration deep plowing method for gardening proposed in the present application determines the abnormality evaluation of each sequence segment in each deep plowing shovel according to the distribution characteristics of the vibration data of each sequence segment of the deep plowing shovel, and analyzes and obtains the vibration correlation degree between the deep plowing shovel and the frame according to the size of the abnormality evaluation combined with the dynamic time regularization matching distance of the vibration data obtained at the same time period as the frame, and then determines the possibility of resonance between the frame and each deep plowing shovel at different frequencies, and obtains the natural frequency range of the frame; that is, by analyzing the performance of real-time vibration data of the vibration-type deep plowing device in a real working environment, the fixed frequency range of the frame is determined, which is more accurate than the natural frequency obtained based only on an idealized theoretical model. In the actual deep plowing process, the fixed frequency range is avoided during the operation of the frame to prevent the frame from resonating.

[0015] 2. The vibrating deep tillage device for gardening provided by the present invention has the same beneficial effects as the vibrating deep tillage method for gardening provided by the present invention, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of the steps of a vibratory deep tillage method for gardening operations provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation, structure, features and effects of the vibrating deep tillage device and method for gardening operations proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the vibrating deep tillage device and method for gardening provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0021] The existing method for determining the natural frequency of the frame in the vibratory deep tillage device usually adopts theoretical calculation methods such as finite element analysis, which is based on an idealized model. There are many influencing factors in the actual deep tillage process, which may cause the vibration generated by the deep tillage shovel cutting the soil to resonate with the frame, causing serious deformation of the frame and affecting the service life of the vibratory deep tillage device; in one embodiment of the present invention, a vibratory deep tillage method for gardening operations is provided, which determines the fixed frequency range of the frame by analyzing the performance of real-time vibration data of the vibratory deep tillage device in a real working environment, and has higher accuracy than the natural frequency obtained based only on the idealized theoretical model. In the actual deep tillage process, the frame avoids the fixed frequency range during operation to prevent the frame from resonating; in order to realize the vibratory deep tillage method for gardening operations, a vibratory deep tillage device for gardening operations is provided, which is essentially an existing vibratory deep tillage device combined with a software system, which is composed of various devices that realize corresponding functions. The specific steps in the method are now introduced in detail.

[0022] See also Figure 1 , which shows a flow chart of the steps of a vibration deep tillage method for gardening provided by an embodiment of the present invention, the method comprising: Step S1: collecting vibration data of a frame and each deep tilling shovel in a vibrating deep tilling device, and performing noise reduction processing on the vibration data to generate a vibration data time series sequence; Step S2: based on the vibration data time series of the deep plowing shovel, the distribution characteristics of the vibration data of each deep plowing shovel are obtained, the abnormal evaluation of each deep plowing shovel is determined, and the vibration correlation degree between the deep plowing shovel and the frame is obtained in combination with the vibration data time series of the frame, and the vibration data time series of the frame and each deep plowing shovel are converted into vibration frequency, and the possibility of any vibration frequency being the natural frequency of the frame is calculated through the vibration frequency and the vibration correlation degree between the deep plowing shovel and the frame; Step S3: Setting a screening threshold, acquiring all vibration frequencies whose probability of being the natural frequency of the frame is greater than the screening threshold, and forming a natural frequency range of the frame.

[0023] To better explain, the vibrating deep loosening device, as a kind of agricultural machinery, is mainly used for deep loosening of the soil to improve the aeration and water permeability of the soil and promote the root development of crops. That is, in actual operation, the device is installed on a tractor or other agricultural machinery, and the deep loosening shovel is inserted into the soil by vibration to achieve the purpose of deep loosening of the soil; the frame is the main supporting structure of the vibrating deep loosening device, bearing the weight of the entire device and ensuring that the device remains stable during operation; the deep loosening shovel is the core working part of the vibrating deep loosening device, which directly contacts the soil and completes the loosening operation. It is usually made of wear-resistant materials to cope with the wear of stones and other hard objects in the soil. Through the vibrating movement of the deep loosening shovel, the hard layer in the soil can be effectively broken, making the soil structure looser, creating good conditions for the growth of crops.

[0024] It is understandable that when performing deep plowing operations, the vibrating deep plowing device cuts the soil with its deep plowing blade. The vibration generated in this process, as well as the vibration frequency of the frame itself, may cause resonance between the deep plowing blade and the frame if the natural frequency range of the deep plowing blade is close to that of the frame. This will cause the frame to be subjected to excessive stress and cause serious deformation of the frame. This phenomenon will not only damage the structural integrity of the frame, but also have an extremely adverse effect on the overall service life of the vibrating deep plowing device. In order to prevent this from happening and ensure the stability and reliability of the vibrating deep plowing device during operation, it is necessary to accurately calculate and analyze the natural frequency range of the frame to effectively avoid resonance between the frame and the deep plowing blade during operation, protect the frame from damage, and extend the service life of the vibrating deep plowing device.

[0025] Furthermore, step S1 includes: The collection frequency is set, and the vibration data of the frame and each deep tillage shovel in the vibratory deep tillage device are collected based on the collection frequency. The vibration data is denoised using median filtering to generate a time series of vibration data.

[0026] As an optional implementation, in this embodiment, the acquisition frequency is set to once per second.

[0027] Specifically, the working environment of the vibratory deep tillage device is often outdoors, so that the acquired vibration data contains interference from the surrounding environment. In order to improve the accuracy of the data, the acquired vibration data is denoised using median filtering, wherein the median filtering is implemented by replacing the value of each data with the median of all data in its neighborhood. It is used to remove random noise in the vibration data, especially those sharp pulse noises, so as to effectively remove or reduce the noise components in the vibration data, thereby generating a more accurate and reliable vibration data time series.

[0028] It can be understood that the vibration data time series of each deep plowing shovel is analyzed to obtain its distribution characteristics, and the distribution characteristics of the local segment vibration data of each deep plowing shovel are compared with the distribution characteristics of the vibration data of other deep plowing shovels in the same period, so as to judge the abnormal evaluation of the local vibration data of each deep plowing shovel, and combine the vibration data of each deep plowing shovel and the frame in the same period to obtain the dynamic time regularization matching distance of the vibration data; determine the possibility of resonance between the frame and each deep plowing shovel at different frequencies, and then determine the natural frequency range of the frame.

[0029] It can be explained that, in the actual working process, different deep plowing shovels generate different vibration data due to different soil conditions, such as soil moisture, soil hardness and soil type. When the soil conditions are abnormal, the deep plowing shovel will show up as abnormal vibration data or obvious changes in vibration data. That is, the vibration data distribution characteristics of any deep plowing shovel on the vibrating deep plowing device in a certain sequence segment are quite different from the vibration data distribution characteristics of other deep plowing shovels in the same sequence segment. Then, the abnormal vibration data performance is likely to be caused by the soil conditions in the area where the deep plowing shovel is located. Therefore, by analyzing the vibration data of the deep plowing shovel, we can indirectly understand and evaluate the physical properties of the soil, which is conducive to analyzing the working parameters of the vibrating deep plowing device to improve the subsequent soil tillage quality.

[0030] Furthermore, in step S2, the distribution characteristics of the vibration data of each deep plowing shovel are obtained based on the time series of the vibration data of the deep plowing shovel, including: The vibration data time series of each deep tiller shovel is equally divided into a number of sequence segments 1, and the amplitude of any sequence segment 1 is determined.

[0031] Optionally, in this embodiment, A deep tillage shovel is taken as an example for explanation, and the vibration data time series sequence is equally divided into 30 sequence segments.

[0032] Specifically, the amplitude values ​​of all data points in each sequence segment one are calculated, and the average of these amplitude values ​​is calculated, and this average is used as the amplitude of each sequence segment one; it can be explained that the vibration data time series sequence is divided into equal parts to facilitate the analysis and processing of a complete vibration data time series sequence with a large amount of data, so as to be able to more accurately analyze the operating status of the data.

[0033] The distribution characteristics of the vibration data of each deep tiller are obtained, and the corresponding calculation formula is: in, Indicates The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates The first subsoiler Sequence segment 1 The amplitude of each data point; Indicates The first subsoiler The amplitude of the sequence segment one; Indicates The first subsoiler The number of data points in a sequence segment; Indicates The first subsoiler The amplitude of the first sequence segment is The mean of the absolute values ​​of the differences in the amplitudes of all remaining sequence segments of a deep tillage shovel.

[0034] It is explained that the distribution characteristics of the vibration data of each deep plowing shovel are obtained, that is, the distribution law and characteristics of the vibration data are obtained based on each sequence segment after equal division, including but not limited to the central tendency, discreteness, skewness and kurtosis of the vibration data, among which the central tendency can reflect the general level of the vibration data of the deep plowing shovel, such as the mean, median and mode; the discreteness describes the fluctuation range and stability of the vibration data; the skewness reveals the symmetry of the vibration data distribution, that is, whether the vibration data distribution is biased to one side; the kurtosis describes the sharpness or flatness of the vibration data distribution; by analyzing the distribution characteristics of the vibration data, support is provided for the subsequent optimization and adjustment of the vibration data.

[0035] It can be explained that The smaller, the The more stable the vibration of a deep tillage shovel is; Indicates The first subsoiler The smaller the difference between the amplitude of each data point in a sequence segment and the overall amplitude of the sequence segment, the smaller the difference, which means that the amplitude of each data point in the sequence segment is very close to the amplitude of the sequence segment, and the vibration amplitude in the corresponding time period of the sequence segment changes very little, which has higher stability.

[0036] Furthermore, in step S2, the abnormal evaluation of each deep tiller is determined, and the corresponding calculation formula is: in, Indicates The first subsoiler Anomaly evaluation of sequence segment one; Indicates The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates that except The second The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates the number of subsoilers on a vibrating subsoiler.

[0037] Make an explanation and determine the abnormal evaluation of each deep plowing shovel, where the abnormal evaluation refers to the performance deviation or failure caused by certain abnormal factors, such as soil conditions, during the use of the deep plowing shovel; by identifying and recording these abnormal evaluations, we can understand the performance of the deep plowing shovel in actual work, and then take corresponding maintenance measures or improve the design to ensure that the deep plowing shovel can complete its working tasks efficiently and stably.

[0038] According to the difference in distribution characteristics of each sequence segment 1 of each deep plowing shovel and the same sequence segment 1 of other deep plowing shovels, the abnormal evaluation of each sequence segment 1 is determined. When the difference in the distribution characteristics of the vibration data of the deep plowing shovel is greater, the corresponding abnormal evaluation is greater; that is, when the first The first subsoiler When the abnormal evaluation of a sequence segment 1 is high, it means that the distribution characteristics of the vibration data of the sequence segment 1 are more different from those of the vibration data of the same sequence segment 1 of other deep tillers. The first subsoiler The more likely the anomaly in the vibration data of a sequence segment is to be affected by the soil conditions, the less likely it is to resonate with the frame.

[0039] Further, in step S2, the vibration correlation degree between the subsoiler and the frame is obtained by combining the vibration data time series of the frame, including: The vibration data time series of the frame is equally divided into a number of sequence segments 2.

[0040] Optionally, in this embodiment, the vibration data time series sequence of the frame is also divided into 30 segments, corresponding to the vibration data time series sequence of the deep tiller.

[0041] The vibration correlation between the deep tiller and the frame is obtained, and the corresponding calculation formula is: in, Indicates The first subsoiler The degree of correlation between each sequence segment and the vibration of the frame; Indicates The first subsoiler Anomaly evaluation of sequence segment one; Indicates The first subsoiler Sequence segment one; Indicates the rack Sequence segment two; express and Dynamic time warping matching distance; The natural constant The exponential function of base .

[0042] The vibration correlation degree between the deep plowing shovel and the frame is explained, wherein the vibration correlation degree indicates the mutual correlation between the vibration of the deep plowing shovel and the frame during operation; the dynamic time warping matching distance, namely DTW (Dynamic TimeWarping, referred to as DTW) matching distance, is a measurement method for measuring the similarity between two time series. By elastically stretching or compressing the time series, the two sequences are aligned on the time axis to find the best matching path. It allows the time series to be nonlinearly deformed on the time axis to eliminate the differences in time scale and more accurately evaluate the similarity between the two sequences.

[0043] It can be shown that the larger the DTW distance value, the The first subsoiler A sequence segment corresponding to the rack The worse the similarity characteristics of the second sequence segment, the The first subsoiler The smaller the sequence segment, the lower the correlation with the vibration of the frame.

[0044] It can be understood that the vibration amplitude of the frame of the vibrating deep plowing device will be directly affected by the vibration amplitude of the deep plowing shovel, because the vibration energy generated by the deep plowing shovel during operation will be transmitted to the frame. When the vibration frequency of the vibrating deep plowing device resonates with the natural frequency of the frame, the transfer of this energy will become more significant, causing the vibration amplitude of the frame to change accordingly with the change of the vibration amplitude of the deep plowing shovel; in other words, if the vibration amplitude of the deep plowing shovel increases, the vibration amplitude of the frame will also increase accordingly, and at the same time, the vibration energy value transmitted to the frame will also increase accordingly.

[0045] Furthermore, in step S2, the vibration data time series of the frame and each deep tiller are converted into vibration frequencies, and the possibility that any vibration frequency is the natural frequency of the frame is calculated by the vibration frequency and the vibration correlation degree between the deep tiller and the frame, including: Step S21: Perform Fourier transform on the vibration data time series of the frame and each deep tiller to obtain the vibration frequency corresponding to each vibration data.

[0046] It is explained that the vibration frequency corresponding to each vibration data is obtained to prepare for the analysis of the frequency domain characteristics of the vibration data time series, so as to understand the dynamic behaviors of the frame and the deep tillage shovel during the operation; wherein, the Fourier transform is used to decompose a complex data, signal or function into a series of simple sine waves.

[0047] Step S22: Filter the vibration frequencies that appear in the frame and each deep plowing shovel to obtain all target vibration frequencies, and obtain the vibration energy value corresponding to each target vibration frequency in the vibration data time series of the frame and each deep plowing shovel, and calculate the possibility of resonance between the frame and the deep plowing shovel based on any target vibration frequency.

[0048] It can be explained that, among all the vibration frequencies corresponding to the vibration data time series of the frame and all the deep plowing shovels, the vibration frequencies that appear in the frame and all the deep plowing shovels are screened and recorded as target vibration frequencies; the vibration energy value corresponding to each target vibration frequency is obtained, wherein the vibration energy value refers to the energy intensity of the vibration data at the target vibration frequency.

[0049] As an optional implementation, in this embodiment, The target vibration frequency is described below.

[0050] Furthermore, in step S22, the possibility of resonance between the frame and the deep tiller based on any target vibration frequency is calculated, and the corresponding calculation formula is: in, Indicates The frame and the first The possibility of resonance of a deep tillage shovel; Indicates The average value of the vibration correlation between all the sequence segments of a deep tiller and the frame; Indicates The vibration data time series of a deep tiller is The vibration energy value corresponding to the target vibration frequency; Indicates the vibration data time series of the rack. The vibration energy value corresponding to the target vibration frequency.

[0051] To illustrate, in the two different vibration data time series of the deep plowing shovel and the frame, when the vibration energy values ​​at the same target vibration frequency are the same, that is, The smaller the The frame and the first The subsoilers have the same amplitude and phase synchronization, that is, The more likely a target vibration frequency is to be a resonant frequency, at which point a special synchronization state is achieved between the frame and the deep tiller.

[0052] Step S23: Determine the possibility that any target vibration frequency is the natural frequency of the frame through the possibility of resonance between the frame and the deep tiller.

[0053] It can be understood that in a vibrating deep plowing device, the frame is the common supporting structure of multiple deep plowing shovels. Therefore, when resonance occurs between one deep plowing shovel and the frame, the impact on each deep plowing shovel is consistent. Similarly, if the resonance causes the performance of a deep plowing shovel to decline or fail, other deep plowing shovels are likely to face the same problem. Therefore, it is necessary to determine the natural frequency of the frame to avoid resonance between the deep plowing shovel and the frame, which would affect the service life of the vibrating deep plowing device.

[0054] Furthermore, in step S23, the possibility of determining that any target vibration frequency is the natural frequency of the frame is calculated as follows: in, Indicates The possibility that the target vibration frequency is the natural frequency of the frame; Indicates The frame and the first The possibility of resonance of a deep tillage shovel; Indicates The frame and the The second The possibility of resonance of a deep tillage shovel; Indicates the number of subsoilers on the vibrating subsoiler; is the normalization function.

[0055] To explain, For the The frame and the first The possibility of resonance of a deep tiller is similar to that of the The second The smaller the difference, the more likely each deep tiller is to be affected by the first The more consistent the impact of the target vibration frequency is, the more There is a high similarity between the target vibration frequency and the natural frequency of the frame, indicating that The greater the probability that a target vibration frequency is the natural frequency of the frame.

[0056] It can be understood that the frame of the vibrating deep tillage device is usually a three-dimensional structure with a certain volume and complex shape, which may be composed of beams, plates, columns and other components of different shapes. The differences in size and stiffness of these components in the horizontal, vertical and front-to-back directions will lead to different vibration characteristics in different directions to produce different natural frequencies. Therefore, there are multiple natural frequencies of the frame of the vibrating deep tillage device, and the range of the natural frequency of the frame is determined to ensure its stability and reliability in actual work.

[0057] Further, step S3 includes: The probability that the target vibration frequency is the natural frequency of the frame is greater than all target vibration frequencies of the screening threshold, forming the natural frequency range of the frame. When the vibrating deep loosening device is working, the natural frequency range of the frame is avoided.

[0058] Preferably, in this embodiment, the screening threshold is set to 0.7.

[0059] Specifically, all target vibration frequencies for which the probability that the target vibration frequency is the natural frequency of the frame is greater than 0.7 constitute the natural frequency range of the frame; in order to prevent the frame of the vibratory deep tillage device from resonating, the natural frequency of the frame is avoided during the deep tillage process; that is, when the vibration sensor on the vibratory deep tillage device detects that the current running vibration frequency of the frame is close to the natural frequency range, the output speed of the power unit is adjusted in time or the gear ratio of the transmission system is changed to reduce or increase the frequency of the vibration source, thereby ensuring that the vibration frequency can be kept away from the natural frequency range of the frame and avoiding the occurrence of resonance.

[0060] It can be understood that the vibration deep plowing method for gardening operations proposed in the present application determines the abnormality evaluation of each sequence segment in each deep plowing shovel according to the distribution characteristics of the vibration data of each sequence segment of the deep plowing shovel, and analyzes and obtains the vibration correlation degree between the deep plowing shovel and the frame according to the size of the abnormality evaluation combined with the dynamic time regularization matching distance of the vibration data obtained in the same period as the frame, and then determines the possibility of resonance between the frame and each deep plowing shovel at different frequencies, and obtains the natural frequency range of the frame; that is, by analyzing the performance of real-time vibration data of the vibration-type deep plowing device in a real working environment, the fixed frequency range of the frame is determined, which is more accurate than the natural frequency obtained only based on an idealized theoretical model. In the actual deep plowing process, the fixed frequency range is avoided during the operation of the frame to prevent the frame from resonating.

[0061] An embodiment of the present invention further proposes a vibrating deep plowing device for gardening operations, which includes a frame and a plurality of deep plowing shovels, and stores program data. When the program data is executed, the frame and the deep plowing shovels are combined to implement any of the aforementioned vibrating deep plowing methods for gardening operations. The device has the same beneficial effects as the aforementioned vibrating deep plowing method for gardening operations, which will not be elaborated here.

[0062] It can be understood that when the various devices of the vibratory deep plowing device used for gardening operations are in operation, it is necessary to utilize the vibratory deep plowing method for gardening operations provided by the aforementioned embodiments. Therefore, whether the frame, deep plowing shovel and program data are integrated or different hardware is configured to produce functions similar to the effects achieved by the present invention, they all fall within the scope of protection of the present invention.

[0063] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A vibrating deep tillage method for gardening, characterized in that: The method comprises: Collect vibration data of the frame and each deep tiller in the vibrating deep tiller, perform noise reduction on the vibration data, and generate a time series of vibration data; Based on the time series of vibration data of the deep plowing shovel, the distribution characteristics of the vibration data of each deep plowing shovel are obtained, and the abnormal evaluation of each deep plowing shovel is determined. The vibration correlation degree between the deep plowing shovel and the frame is obtained by combining the time series of vibration data of the frame, and the time series of vibration data of the frame and each deep plowing shovel is converted into vibration frequency. The possibility of any vibration frequency being the natural frequency of the frame is calculated through the vibration frequency and the vibration correlation degree between the deep plowing shovel and the frame; A screening threshold is set to obtain all vibration frequencies whose probability of being the natural frequency of the frame is greater than the screening threshold, thereby forming a natural frequency range of the frame.

2. The vibrating deep tillage method for gardening as claimed in claim 1, characterized in that: The vibration data of the frame and each deep plowing shovel in the vibratory deep plowing device are collected, and the vibration data are subjected to noise reduction processing to generate a vibration data time series sequence, including: setting a collection frequency, collecting the vibration data of the frame and each deep plowing shovel in the vibratory deep plowing device based on the collection frequency, using median filtering to perform noise reduction processing on the vibration data, and generating a vibration data time series sequence.

3. The vibrating deep tillage method for gardening as claimed in claim 1, characterized in that: Based on the vibration data time series of the deep plowing shovel, the distribution characteristics of the vibration data of each deep plowing shovel are obtained, including: The vibration data time series of each deep tiller is equally divided into a number of sequence segments 1, and the amplitude of any sequence segment 1 is determined; The distribution characteristics of the vibration data of each deep tiller are obtained, and the corresponding calculation formula is: in, Indicates The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates The first subsoiler Sequence segment 1 The amplitude of each data point; Indicates The first subsoiler The amplitude of the sequence segment one; Indicates The first subsoiler The number of data points in a sequence segment; Indicates The first subsoiler The amplitude of the first sequence segment is The mean of the absolute values ​​of the differences in the amplitudes of all remaining sequence segments of a deep tillage shovel.

4. The vibrating deep tillage method for gardening as claimed in claim 3, characterized in that: Determine the abnormal evaluation of each deep tiller, and the corresponding calculation formula is: in, Indicates The first subsoiler Anomaly evaluation of sequence segment one; Indicates The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates that except The second The first subsoiler Distribution characteristics of vibration data of a sequence segment; Indicates the number of subsoilers on a vibrating subsoiler.

5. The vibrating deep tillage method for gardening as claimed in claim 4, characterized in that: The vibration correlation between the subsoiler and the frame is obtained by combining the frame vibration data time series, including: The vibration data time series of the rack is equally divided into a plurality of sequence segments 2; The vibration correlation between the deep tiller and the frame is obtained, and the corresponding calculation formula is: in, Indicates The first subsoiler The degree of correlation between each sequence segment and the vibration of the frame; Indicates The first subsoiler Anomaly evaluation of sequence segment one; Indicates The first subsoiler Sequence segment one; Indicates the rack Sequence segment two; express and Dynamic time warping matching distance; The natural constant The exponential function of base .

6. The vibrating deep tillage method for gardening as claimed in claim 5, characterized in that: The vibration data time series of the frame and each deep tiller are converted into vibration frequency, and the possibility of any vibration frequency being the natural frequency of the frame is calculated by the vibration frequency and the vibration correlation degree between the deep tiller and the frame, including: Perform Fourier transform on the vibration data time series of the frame and each deep tiller to obtain the vibration frequency corresponding to each vibration data; Filter the vibration frequencies that appear in the frame and each deep tiller to obtain all target vibration frequencies, obtain the vibration energy value corresponding to each target vibration frequency in the vibration data time series of the frame and each deep tiller, and calculate the possibility of resonance between the frame and the deep tiller based on any target vibration frequency; The possibility of resonance between the frame and the deep tiller is used to determine the possibility that any target vibration frequency is the natural frequency of the frame.

7. The vibrating deep tillage method for gardening as claimed in claim 6, characterized in that: The calculation formula for calculating the possibility of resonance between the frame and the deep tiller based on any target vibration frequency is: in, Indicates The frame and the first The possibility of resonance of a deep tillage shovel; Indicates The average value of the vibration correlation between all the sequence segments of a deep tiller and the frame; Indicates The vibration data time series of a deep tiller is The vibration energy value corresponding to the target vibration frequency; Indicates the vibration data time series of the rack. The vibration energy value corresponding to the target vibration frequency.

8. The vibrating deep tillage method for gardening as claimed in claim 6, characterized in that: Determine the possibility of any target vibration frequency being the natural frequency of the frame. The corresponding calculation formula is: in, Indicates The possibility that the target vibration frequency is the natural frequency of the frame; Indicates The frame and the first The possibility of resonance of a deep tillage shovel; Indicates The frame and the The second The possibility of resonance of a deep tillage shovel; Indicates the number of subsoilers on the vibrating subsoiler; is the normalization function.

9. The vibrating deep tillage method for gardening as claimed in claim 6, characterized in that: Acquire all vibration frequencies whose probability of being the natural frequency of the frame is greater than a screening threshold to form a natural frequency range of the frame, including: screening all target vibration frequencies whose probability of being the natural frequency of the frame is greater than a screening threshold to form a natural frequency range of the frame, and avoid the natural frequency range of the frame when the vibrating deep tillage device is working.

10. A vibrating deep tillage device for gardening, characterized in that: The device includes a frame and a plurality of deep plowing shovels, and stores program data. When the program data is executed, the frame and the deep plowing shovels are combined to implement the vibration deep plowing method for gardening operations as described in any one of claims 1-9.

Images