CN117350135A - Frequency band expanding method and system of hybrid energy collector - Google Patents
Frequency band expanding method and system of hybrid energy collector Download PDFInfo
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Abstract
The invention discloses a frequency band expanding method and a system of a hybrid energy collector, wherein the method comprises the following steps: constructing a dynamic model of the hybrid energy collector according to the frequency distribution during wheel track vibration, and analyzing the frequency band of the hybrid energy collector according to the dynamic model; introducing uncertainty into the attention weight of the dynamic model according to Bayesian variation learning, and constructing an uncertain hybrid energy collector model; training an uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain size parameters of each level of vibration pickup structure of the hybrid energy collector; and solving structural parameters of an uncertain hybrid energy collector model according to the dimensional parameters of each vibration pickup structure, and simulating each vibration pickup system in the hybrid energy collector according to the solving result to obtain the resonant frequency of each vibration pickup system after frequency expansion. The frequency matching capability of the energy collector is enhanced, and the power supply problem of the trackside microelectronic device is solved.
Description
Technical Field
The invention belongs to the technical field of vibration energy collection, and particularly relates to a frequency band expanding method and system of a hybrid energy collector.
Background
In the field of railway traffic, the demand for sensing devices for monitoring facilities such as rail vehicles, railways and the like is growing, and the rail monitoring sensing and microelectronic devices are required to be externally powered. At present, the conventional external power supply mode cannot meet the power supply requirement of the trackside electronic equipment.
Vibration energy harvesters can provide an effective power supply solution for on-board sensors and are therefore widely recognized as a new power supply technology for research on trackside sensing electronics. However, in the practical application process, since the vibration frequency of the wheel track is distributed in a plurality of frequency bands, the working frequency band range of the general energy harvester is narrow, and frequency matching with the vibration frequency of the rail vehicle in a wide frequency range is difficult to realize, so that the energy collection efficiency is affected. In terms of vibration energy harvesting, the former have proposed some methods to expand the bandwidth of the frequency band. The invention is disclosed in Chinese patent with publication number CN111130387A as an asymmetric composite type broadband vibration energy collector: by utilizing the nonlinear characteristic of the structure, the frequency band expansion of the energy collector can be realized. The invention is disclosed in Chinese patent with publication number of CN108549100A, which is a time domain multi-scale full waveform inversion method based on nonlinear high-order frequency expansion: the multi-scale full waveform inversion method is proposed to widen the frequency band of the seismic record, and the full waveform inversion based on the frequency band can effectively avoid local extremum solutions.
In the above methods, although the frequency band can be widened to some extent, there are certain limitations. The existing algorithm has some defects: (1) Is easy to be influenced by weight, threshold value and learning rate to be trapped in local optimum; (2) processing high-dimensional data is less accurate.
Disclosure of Invention
The invention provides a frequency band expanding method and a system of a hybrid energy collector, which are used for solving the technical problems that the working frequency band range of a general energy harvester is narrow, and frequency matching with the vibration frequency of a railway vehicle in a wide frequency range is difficult to realize.
In a first aspect, the present invention provides a method for expanding the frequency band of a hybrid energy collector, comprising: constructing a dynamic model of a hybrid energy collector according to frequency distribution during wheel-rail vibration, and analyzing a frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system; introducing uncertainty into the attention weight of the dynamic model according to Bayesian variation learning, and constructing an uncertain hybrid energy collector model; training the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the size parameters of each stage of vibration pickup structure of the hybrid energy collector; and solving structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulating each vibration pickup system part in the hybrid energy collector according to a solving result to obtain the resonant frequency of each vibration pickup system after frequency expansion.
In a second aspect, the present invention provides a band expansion system of a hybrid energy collector, comprising: the system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is configured to construct a dynamic model of a hybrid energy collector according to frequency distribution during wheel track vibration, and analyze the frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system; a second construction module configured to introduce uncertainty into the attention weight of the kinetic model according to bayesian variation learning, constructing an uncertainty hybrid energy collector model; the training module is configured to train the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the size parameters of each level of vibration pickup structure of the hybrid energy collector; and the solving module is configured to solve the structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulate each vibration pickup system part in the hybrid energy collector according to the solving result to obtain the resonance frequency of each vibration pickup system after frequency expansion.
In a third aspect, there is provided an electronic device, comprising: the hybrid energy collector comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the hybrid energy collector's band expansion method of any of the embodiments of the present invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, causes the processor to perform the steps of the method for band expansion of a hybrid energy collector of any of the embodiments of the present invention.
According to the frequency band expansion method and system of the hybrid energy collector, the dynamic model of the piezoelectric-electromagnetic hybrid energy collector is built according to the frequency distribution in a plurality of frequency bands when the wheel track vibrates, the frequency bands are analyzed, main structural parameters in the model are solved, uncertainty is introduced into the attention weight of the hybrid energy collector model by means of Bayesian variation learning, the uncertainty hybrid energy collector model is built, simulation is carried out on all stages of vibration pickup systems in the piezoelectric-electromagnetic hybrid energy collector, the resonance frequency of all stages of vibration pickup systems after frequency expansion is obtained, the frequency matching capacity of the energy collector is enhanced, and the power supply problem of the trackside microelectronic equipment is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for expanding the frequency band of a hybrid energy collector according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a hybrid energy collector according to an embodiment of the present invention;
FIG. 3 is a block diagram illustrating a hybrid energy collector band expansion system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flow chart of a method for expanding the frequency band of a hybrid energy collector is shown.
As shown in fig. 1, the method for expanding the frequency band of the hybrid energy collector specifically includes the following steps:
step S101, a dynamic model of a hybrid energy collector is constructed according to frequency distribution during wheel track vibration, and the frequency band of the hybrid energy collector is analyzed according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system.
In this step, as shown in fig. 2, the first vibration pickup system includes a cross cantilever structure and a permanent magnet as a mass block, wherein PVDF piezoelectric films are attached to the upper and lower ends of the cross cantilever to form a piezoelectric structure; the second vibration pickup system and the third vibration pickup system are respectively vibratable plane springs with different structures, and a hybrid energy collector is constructed through the first vibration pickup system, the second vibration pickup system and the third vibration pickup system.
It should be noted that the expression of the kinetic model of the hybrid energy collector is:
,
in the method, in the process of the invention,、/>、/>the equivalent mass blocks of the first vibration pickup system, the second vibration pickup system and the third vibration pickup system respectively are +. >、/>、/>The mechanical damping of the first vibration pickup system, the mechanical damping of the second vibration pickup system and the mechanical damping of the third vibration pickup system are respectively +.>For equivalent electromagnetic damping between the first vibration pickup system and the third vibration pickup system, +.>Equivalent piezoelectric damping for the second vibration pickup system, < >>、/>、/>The linear elastic coefficient in the first vibration pickup system, the linear elastic coefficient in the second vibration pickup system and the linear elastic coefficient in the third vibration pickup system are respectively +.>、/>、/>Respectively, the nonlinear elastic coefficient in the first vibration pickup system, the nonlinear elastic coefficient in the second vibration pickup system and the nonlinear elastic coefficient in the third vibration pickup system,/and->、/>、/>The spring deformation quantity in the first vibration pickup system, the spring deformation quantity in the second vibration pickup system and the spring deformation quantity in the third vibration pickup system are respectively +.>、/>、/>The spring deformation acceleration in the first vibration pickup system, the spring deformation acceleration in the second vibration pickup system and the spring deformation acceleration in the third vibration pickup system are respectively +.>、/>、/>The deformation speed of the spring in the first vibration pickup system, the deformation speed of the spring in the second vibration pickup system and the deformation speed of the spring in the third vibration pickup system are respectively +.>Exciting acceleration for the outside;
the dynamic equation for a single vibration pickup system can be expressed as:
，
In the method, in the process of the invention,is the equivalent mass block of the vibration pickup system, +.>For mechanical damping of the vibration-pickup system +.>Is a linear elastic coefficient of the elastic modulus,is nonlinear elastic coefficient +>For the relative acceleration of the vibration pickup system, +.>For the relative speed of the vibration pickup system, +.>Is the relative displacement of the vibration pickup system;
when the outside is excited toWhen the motion equation of the nonlinear vibration pickup system is expressed as follows:
，
in the method, in the process of the invention,for the external excitation amplitude +.>Is equivalent to the total damping ratio->For frequency +.>Time is;
simultaneously dividing the expression of the motion equation of the nonlinear vibration pickup system by the equivalent mass of the vibration pickup systemThe dimensionless expression of the motion equation of the nonlinear vibration pickup system is obtained as follows:
，
in the method, in the process of the invention,acceleration of equivalent mass +.>For the total damping ratio->For the speed of equivalent mass, +.>For displacement of equivalent mass->Is nonlinear and>to normalize the frequency，/>For the phase +.>Is the phase angle between the external stimulus and the response;
assuming steady state solutionThe expressions of the speed of the equivalent mass block of the vibration pickup system and the acceleration of the equivalent mass block of the vibration pickup system can be obtained according to the steady state solution, respectively, are:
，
，
substituting the speed of the equivalent mass block of the vibration pickup system, the acceleration and the steady state solution of the equivalent mass block of the vibration pickup system into a dimensionless expression of a motion equation of the nonlinear vibration pickup system to obtain a dimensionless target expression as follows:
，
Simplifying the dimensionless target expression according to a harmonic balancing method to obtain a nonlinear approximate solution of the vibration pickup system, wherein the nonlinear approximate solution is as follows:
，
，
obtaining an expression between the amplitude and the frequency of the nonlinear vibration pickup system under forced vibration:
，
in the method, in the process of the invention,is the damping ratio;
when (when)When the vibration pickup system is in a linear vibration state, the expression between the amplitude and the frequency of the nonlinear vibration pickup system is as follows:
，
in the method, in the process of the invention,the amplification factor of the linear vibration pickup system is the ratio of the response amplitude to the input excitation amplitude of the vibration pickup system in a steady state;
when the natural frequency of the vibration pickup systemThe amplitude of the vibration pickup system is as follows:
。
it should be noted that when the amplitude of the steady-state response of the linear vibration pickup system is reduced to the peak value of the responseWhen the frequency is multiplied, the difference between the two corresponding frequency points is defined as the linear frequency bandwidth of the vibration pickup system, wherein the expression for calculating the linear frequency bandwidth is as follows:
，
in the method, in the process of the invention,is linear bandwidth, < >>For peak value corresponding frequency point, < >>Is +.>Corresponding to frequency point at times->For damping ratio->For resonance frequency +.>Is a mechanical damping ratio->Is an electromagnetic damping ratio;
the method comprises the steps of respectively squaring and then adding an expression of a nonlinear approximate solution of the vibration pickup system and an expression between the amplitude and the frequency of the nonlinear vibration pickup system under forced vibration to obtain a nonlinear amplitude-frequency relation of the vibration pickup system:
，
In the method, in the process of the invention,for amplitude of vibration pickup system +.>Is nonlinear and>for normalizing frequency, ++>Is the total damping ratio;
solving a nonlinear amplitude-frequency relation of the vibration pickup system to obtain a relation of the amplitude and the frequency of the vibration pickup system:
，
due to damping ratio in hybrid energy collectorFar less than 1, the relation between the amplitude and the frequency of the vibration pickup system is expressed as:
，
，
in the method, in the process of the invention,corresponding frequency of the first amplitude of the steady-state response of the resonant branch, +.>A corresponding frequency that is a second amplitude of the resonant branch steady state response;
from the expression of the corresponding frequency of the first amplitude of the steady-state response of the resonant branch, whenWith maximum value, equation->Establishment;
pair equationSolving to obtain a relation of maximum amplitude, nonlinearity and damping ratio:
，
in the method, in the process of the invention,is the maximum amplitude;
will beSubstituting the expression of the corresponding frequency of the first amplitude of the steady-state response of the resonance branch into the expression of the corresponding normalized frequency is obtained:
，
in the method, in the process of the invention,is the maximum frequency;
according toDeducing the amplitude drop to +.>Expression of amplitude at time:
，
in the method, in the process of the invention,to decrease to +.>Amplitude at time;
reducing the amplitude toThe amplitude at that time is substituted into the expression of the corresponding frequency of the first amplitude of the steady-state response of the resonant branch to obtain +. >Wherein ∈0 is calculated>The expression of (2) is:
，
in the method, in the process of the invention,to decrease to +.>Frequency at time;
according toAnd->Determining a nonlinear frequency bandwidth of a vibration pickup system in a hybrid energy collector, wherein an expression for calculating the nonlinear frequency bandwidth is:
，
in the method, in the process of the invention,is a nonlinear frequency bandwidth.
Step S102, uncertainty is introduced into the attention weight of the dynamic model according to Bayesian variation learning, and an uncertain hybrid energy collector model is constructed.
In this step, the uncertain hybrid energy collector model consists of a convolutional layer, of a plurality of stacked encoders, and of a plurality of classifiers stacked in fully connected layers. Specifically, the uncertain hybrid energy collector model consists of a designed probabilistic attention, a multi-layer perceptron (MLP), two layer normalization layers, and two residual connections.
The multi-head self-attention mechanism firstly processes the original Q, K, V through H groups of different linear projections to obtain H different versions Q, K, V, and then carries out the proportional dot product attention operation on each version Q, K, V to obtain H outputs. Finally, these H output connections and predictive linearities again obtain the final output, the formula for the multi-headed attentiveness mechanism is as follows:
，
，
In the method, in the process of the invention,for the multi-head self-attention mechanism, < >>For the first attention head, +.>For the second attention head +.>The H-th attention head is, < ->For the first attention head, +.>、/>、/>、/>Parameter matrix to be learned for model, +.>For the connection function +.>Is a self-attention mechanism;
the posterior distribution can be generally solved using bayesian rules:
，
in the method, in the process of the invention,for a given training set D +.>Probability of establishment->Assumption +.>Is>For normalizing constant, ++>For posterior probability>Parameters of data distribution;
since neural networks typically contain a large number of parameters, soBecomes complex, requires variational reasoning to capture the approximate distribution, and generally selects Kullback-Leibler (KL) divergence as a measure:
，
in the method, in the process of the invention,to minimize the variation distribution +.>As a matter of evidence, the information of the presence of the substance,
further defined as:
，
in the method, in the process of the invention,is the lower bound of evidence.
And step S103, training the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the optimal size of the hybrid energy collector.
In this step, a data sample is generated by finite element software;
dividing each data sample into m-1 signal segments and passing through a convolution layer Projecting each signal segment to a dimension +.>Thereby obtaining a mark embedded layer, wherein +_>For embedding signal sections, 1 and +.>Respectively representing the number of input channels and the number of output channels, < ->、/>Respectively representing the size of the convolution kernel and the shift step length;
will be a learnable oneThe embedded class is connected to->To obtain the signal section embedding +.>And at->Add an insert->Is to obtain the mark embedding +.>Wherein->，/>For classification errors +.>For a desired classification error;
will beFeeding into an encoder to obtain the hidden characteristic of the sample>Hidden feature->The expression of (2) is:
，
in the method, in the process of the invention,embedding +.>Is a hidden layer of the first layer-1,/a>Conceal layer for layer I->Is a multi-layer sensor>To pay attention to the +.>The layer of the material is formed from a layer,layer normalization for applying attention mechanisms;
extraction ofThe transformed signal segments are used as target hidden features to classify, and based on the classified target hidden features, the uncertain hybrid energy collector model is iteratively trained to obtain the optimal size of the hybrid energy collector, wherein the classification process can be described as follows:
，
in the method, in the process of the invention, To predict the probability distribution of the tag +.>For the expected classification error at L +.>To indicate that the desired classification error at L is entered into the fully connected layer for processing,/>For->Application ofThe function is activated.
A variational posterior distribution reasoning network, according to the average field theory,can be decomposed into products of L Gaussian distributions:
，
in the method, in the process of the invention,to introduce a parametric reasoning model, +.>For the value range>Is Gaussian matrix->And->Are respectively by->A matrix of mean and standard deviation of the gaussian distribution.
To obtain two distribution parametersAnd->With Q, K, V as input, first pass +.>And obtaining a non-normalized weight. Then, deriving +.A.A posterior inference network consisting of two layers of perceptrons of probabilistic interest is used>And->：
，
，
Where f represents a linear projection and ReLU represents an activation function.
Taking into account the random weights of the samplesIs not conductive and the standard deviation must be non-negative, the sampling effect is equivalent to using a re-parameterization technique:
，
in the method, in the process of the invention,random weighting for samples>To re-parameterize the sample +_>For (I)>、/>Are sample matrixes;
finally, through al=softmax%) A randomly normalized attention weight is obtained.
Priori distributed inference network to avoid over-fitting, the priori inference network is chosen to derive the mean of Gaussian priors and the standard deviation is given directly as a super-parameter. According to the average field theory, this a priori can be defined as:
，
In the method, in the process of the invention,for the joint distribution of observation data and latent variables, +.>For regularization coefficient, ++>Is made of->Matrix of derived shared mean +.>Is a matrix with elements of a given standard deviation, < >>The reasoning process can be described as:
，
optimization objective definition by minimizingUpdating parameters of the uncertain hybrid energy collector model, equivalent to maximizing the lower bound of evidence:
，
in the method, in the process of the invention,optimizing targets for model->To minimize the variation distribution expectations, +.>Log-edge likelihood for joint distribution, +.>As regularization coefficients, it can be further expressed as:
，
rewriting the evidence lower bound target as:
，
in the method, in the process of the invention,for the number of samplings>For cross entropy loss, < >>To approximate posterior distribution function->Is a parameter vector.
And step S104, solving the structural parameters of the uncertain hybrid energy collector model according to the optimal size, and simulating each vibration pickup system part in the hybrid energy collector according to the solving result to obtain the resonant frequency of each vibration pickup system after frequency expansion.
In the step, the elastic coefficient of the first vibration pickup system is equivalent to the sum of the elastic coefficients of four folding beams with double-end guiding boundary conditions;
in the folding supporting beam, n sections of cross beams are arranged, and n-1 sections of vertical beams are correspondingly arranged, wherein Liang Wanju and torque calculation formulas of each section are as follows:
，
，
，
In the method, in the process of the invention,is the bending moment of the middle cross beam of the folding beam, +.>To balance bending moment around Y-axis +.>Force in the Z-axis direction +.>Numbering the cross beam>For the length of the beam->Is a spring beam variable +.>For the torque of the cross beam->In order to balance the torque about the X-axis,is the bending moment of the vertical beam, is->For (I)>Torque for vertical beam，/>Numbering the vertical beams>For the length of the vertical beam->Bending moment of the vertical beam of n-1 sections;
since the cross-sectional areas of the beams are equal, the moment of inertia of the cross-beam about the y-axis and the vertical beam about the x-axis are equal, i.e；
The total deformation of the whole beam is expressed as:
，
in the method, in the process of the invention,for the torsion factor along the Y-axis, +.>Torsional coefficient of rectangular section, +.>For modulus of elasticity>For shear modulus of elasticity, < >>For the torsion factor along the X-axis, +.>Bending moment of n-1 section vertical beam, < ->Torque for n-2 section vertical beams;
wherein, the expression for calculating the torsion coefficient of the rectangular section is:
，
in the method, in the process of the invention,for thickness (S)>For width (S)>Is the elastic coefficient;
the expression for calculating the shear modulus of elasticity is:
，
in the method, in the process of the invention,is poisson's ratio;
force under forceUnder the action, the expression of the deformation of the beam tail end in the Z-axis direction is as follows:
，
in the method, in the process of the invention,is the deformation in the Z-axis direction +.>Is the elastic coefficient in the Z direction, +.>Is a Z-axis force;
According to the forceUnder the action, the deformation of the beam tail end in the Z-axis direction is calculated to obtain the linear elastic coefficient +.>Wherein the linear spring rate in the first vibration pickup system is calculated +.>The expression of (2) is:
，
the supporting beam in the second vibration pickup system is regarded as a folding beam with equal vertical beam length, and in the U-shaped supporting beam, the calculation formulas of bending moment and torque of each section of beam are as follows:
，
，
，
，
in the method, in the process of the invention,to balance bending moment around Y-axis +.>Is the torque at s 1->Is the bending moment of the beam at the first position 1;
the expression of the total deformation of the whole U-shaped supporting beam is as follows:
，
in the method, in the process of the invention,is the bending moment of the beam at the s1 position, < ->For the Y-axis torsion coefficient at s1, < ->For a Y-axis torsion coefficient at s3, < >>For the torsion factor about the X-axis at s3, < >>Is s2 bending moment>For s2 beam torque,/->For the Y-axis torsion coefficient at s2, < ->For the torsion factor about the X-axis at s2, < >>Is the bending moment of the nth section beam, and is->For the torque of the nth segment,for the torsion factor about the X-axis at l1, < >>For the Y-axis torsion coefficient at l1, < ->For the beam bend at l2Moment (I)>For the beam torque at l1,/->For the torsion factor about the X-axis at l2, < >>A twist factor about the Y axis at l 2;
wherein,，
，
，
，
deriving linear elastic coefficient in second vibration pickup system The method comprises the following steps:
，
in the method, in the process of the invention,for n-segment shear modulus of elasticity, < >>Width of girder>Is the length of the vertical beam;
dividing the end of the support beam in the third vibration pickup system into torque around the X-axisAnd bending moment of Y-axis>The bending moment and the torque of each section of beam are respectively as follows:
，
in the method, in the process of the invention,for torque at>Torque at s>Bending moment at position I, I>Is the bending moment of the Y axis, +.>Is the bending moment at the s position,a bending moment at c;
deriving linear elastic coefficient in third vibration pickup system by the same methodThe expression of (2) is:
，/>
in the method, in the process of the invention,for modulus of elasticity at s>For modulus of elasticity at c>For the shear modulus at c, +.>For the shear modulus at l, +.>For modulus of elasticity at l, +.>For modulus of elasticity at t>For the shear modulus at t>Shear modulus at s, +.>For the modulus of elasticity at r>For modulus of elasticity at i>The shear modulus of elasticity at i.
It should be noted that, the nonlinear analysis of the first vibration pickup system:
and applying load force to the geometric center of the first vibration pickup system through finite element analysis software, comparing nonlinear deformation of the first vibration pickup system under different loads, and calculating the obtained linear and nonlinear elastic coefficients through a least square method. The rewriteable is:
，
calculating the nonlinearity of the system under different acceleration excitation :
，
Nonlinear analysis of the second vibration pickup system:
and similarly, applying a load force to the geometric center of the vibration pickup system II, comparing nonlinear deformation of the second vibration pickup system under different loads, and calculating the linear and nonlinear elastic coefficients through a least square method. The rewriteable is:
，
non-linearities of systems under different acceleration excitations:
，
Nonlinear analysis of the third vibration pickup system:
and similarly, applying a load force to the geometric center of the third vibration pickup system, comparing nonlinear deformation of the third vibration pickup system under different loads, and calculating the linear and nonlinear elastic coefficients through a least square method. The rewriteable is:
，
non-linearities of systems under different acceleration excitations:
。
In summary, the three resonant frequencies of the prototype were all within the range of design frequency requirements of 40.5Hz, 68.2Hz, and 146 Hz. When the loading acceleration is increased from 0.5g to 1.5g, nonlinearity occurs in both the first vibration pickup system and the second vibration pickup system in the prototype, the frequency bandwidth of the multimode nonlinear miniature electromagnetic vibration energy collector at the first-order frequency and the second-order frequency is obviously increased, and the half-power bandwidth is increased from 4Hz to 10.1Hz at the first-order resonance frequency; at the second order resonance frequency, the frequency bandwidth increases from 4Hz to 7.8Hz. The third order frequency of the prototype remains substantially unchanged as the acceleration increases,
According to the method, frequency distribution is carried out on a plurality of frequency bands according to wheel track vibration, a dynamic model of the piezoelectric-electromagnetic hybrid energy collector is established, the frequency bands are analyzed, main structural parameters in the model are solved, uncertainty is introduced into the attention weight of the hybrid energy collector model through Bayesian variation learning, the uncertainty hybrid energy collector model is constructed, simulation is carried out on all stages of vibration pickup systems in the piezoelectric-electromagnetic hybrid energy collector, resonance frequencies of all stages of vibration pickup systems after frequency expansion are obtained, the frequency matching capacity of the energy collector is enhanced, and the power supply problem of the trackside microelectronic equipment is solved.
Referring to fig. 3, a block diagram of a hybrid energy collector band expansion system of the present application is shown.
As shown in fig. 3, the band expansion system 200 includes a first building block 210, a second building block 220, a training block 230, and a solution block 240.
The first construction module 210 is configured to construct a dynamic model of the hybrid energy collector according to the frequency distribution when the wheel track vibrates, and analyze the frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system; a second construction module 220 configured to introduce uncertainty into the attention weight of the kinetic model according to bayesian variation learning, constructing an uncertainty hybrid energy collector model; a training module 230 configured to train the uncertain hybrid energy collector model by means of data samples generated by finite element simulation, to obtain dimensional parameters of each stage of vibration pickup structure of the hybrid energy collector; and the solving module 240 is configured to solve the structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulate each vibration pickup system part in the hybrid energy collector according to the solving result to obtain the resonance frequency of each vibration pickup system after frequency expansion.
It should be understood that the modules depicted in fig. 3 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 3, and are not described here again.
In other embodiments, the present invention further provides a computer readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to perform the method for expanding the frequency band of the hybrid energy collector in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
constructing a dynamic model of a hybrid energy collector according to frequency distribution during wheel-rail vibration, and analyzing a frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system;
introducing uncertainty into the attention weight of the dynamic model according to Bayesian variation learning, and constructing an uncertain hybrid energy collector model;
Training the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the size parameters of each stage of vibration pickup structure of the hybrid energy collector;
and solving structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulating each vibration pickup system part in the hybrid energy collector according to a solving result to obtain the resonant frequency of each vibration pickup system after frequency expansion.
The computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of the hybrid energy harvester's band expansion system, etc. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located with respect to the processor, the remote memory being connectable to the band expansion system of the hybrid energy harvester via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 4, where the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 4. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running non-volatile software programs, instructions and modules stored in the memory 320, i.e. implements the above-described method of band expansion of the hybrid energy collector. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the band expansion system of the hybrid energy harvester. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
As an embodiment, the electronic device is applied to a frequency band expansion system of a hybrid energy collector, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
constructing a dynamic model of a hybrid energy collector according to frequency distribution during wheel-rail vibration, and analyzing a frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system;
introducing uncertainty into the attention weight of the dynamic model according to Bayesian variation learning, and constructing an uncertain hybrid energy collector model;
training the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the size parameters of each stage of vibration pickup structure of the hybrid energy collector;
and solving structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulating each vibration pickup system part in the hybrid energy collector according to a solving result to obtain the resonant frequency of each vibration pickup system after frequency expansion.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for expanding the frequency band of a hybrid energy collector, comprising:
constructing a dynamic model of a hybrid energy collector according to frequency distribution during wheel-rail vibration, and analyzing a frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system;
introducing uncertainty into the attention weight of the dynamic model according to Bayesian variation learning, and constructing an uncertain hybrid energy collector model;
training the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the size parameters of each stage of vibration pickup structure of the hybrid energy collector;
and solving structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulating each vibration pickup system part in the hybrid energy collector according to a solving result to obtain the resonant frequency of each vibration pickup system after frequency expansion.
2. The method for expanding the frequency band of the hybrid energy collector according to claim 1, wherein the first vibration pickup system comprises a cross-shaped cantilever structure and a permanent magnet serving as a mass block, and PVDF piezoelectric films are attached to the upper end and the lower end of the cross-shaped cantilever to form a piezoelectric structure;
The second vibration pickup system and the third vibration pickup system are respectively vibratable plane springs with different structures.
3. The method for expanding the frequency band of the hybrid energy collector according to claim 1, wherein the expression of the kinetic model of the hybrid energy collector is:
,
in the method, in the process of the invention,、/>、/>the equivalent mass blocks of the first vibration pickup system, the second vibration pickup system and the third vibration pickup system respectively are +.>、/>、/>The mechanical damping of the first vibration pickup system, the mechanical damping of the second vibration pickup system and the mechanical damping of the third vibration pickup system are respectively +.>For equivalent electromagnetic damping between the first vibration pickup system and the third vibration pickup system,equivalent piezoelectric damping for the second vibration pickup system, < >>、/>、/>The linear elastic coefficient in the first vibration pickup system, the linear elastic coefficient in the second vibration pickup system and the linear elastic coefficient in the third vibration pickup system are respectively +.>、/>、/>Respectively, the nonlinear elastic coefficient in the first vibration pickup system, the nonlinear elastic coefficient in the second vibration pickup system and the nonlinear elastic coefficient in the third vibration pickup system,/and->、/>、/>The spring deformation quantity in the first vibration pickup system, the spring deformation quantity in the second vibration pickup system and the spring deformation quantity in the third vibration pickup system are respectively +. >、/>、/>The spring deformation acceleration in the first vibration pickup system, the spring deformation acceleration in the second vibration pickup system and the spring deformation acceleration in the third vibration pickup system are respectively +.>、/>、/>The deformation speed of the spring in the first vibration pickup system, the deformation speed of the spring in the second vibration pickup system and the deformation speed of the spring in the third vibration pickup system are respectively +.>Exciting acceleration for the outside;
the dynamic equation for a single vibration pickup system can be expressed as:
，
in the method, in the process of the invention,is the equivalent mass block of the vibration pickup system, +.>For mechanical damping of the vibration-pickup system +.>Is linear elastic coefficient>Is nonlinear elastic coefficient +>For the relative acceleration of the vibration pickup system, +.>For the relative speed of the vibration pickup system, +.>For vibration-pickup systemsA relative displacement;
when the outside is excited toWhen the motion equation of the nonlinear vibration pickup system is expressed as follows:
，
in the method, in the process of the invention,for the external excitation amplitude +.>Is equivalent to the total damping ratio->For frequency +.>Time is;
simultaneously dividing the expression of the motion equation of the nonlinear vibration pickup system by the equivalent mass of the vibration pickup systemThe dimensionless expression of the motion equation of the nonlinear vibration pickup system is obtained as follows:
，
in the method, in the process of the invention,acceleration of equivalent mass +.>For the total damping ratio- >For the speed of equivalent mass, +.>For displacement of equivalent mass->Is nonlinear and>for normalizing frequency, ++>For the phase +.>Is the phase angle between the external stimulus and the response;
assuming steady state solutionThe expressions of the speed of the equivalent mass block of the vibration pickup system and the acceleration of the equivalent mass block of the vibration pickup system can be obtained according to the steady state solution, respectively, are:
，
，
substituting the speed of the equivalent mass block of the vibration pickup system, the acceleration and the steady state solution of the equivalent mass block of the vibration pickup system into a dimensionless expression of a motion equation of the nonlinear vibration pickup system to obtain a dimensionless target expression as follows:
，
simplifying the dimensionless target expression according to a harmonic balancing method to obtain a nonlinear approximate solution of the vibration pickup system, wherein the nonlinear approximate solution is as follows:
，
，
obtaining an expression between the amplitude and the frequency of the nonlinear vibration pickup system under forced vibration:
，
in the method, in the process of the invention,is the damping ratio;
when (when)When the vibration pickup system is in a linear vibration state, the expression between the amplitude and the frequency of the nonlinear vibration pickup system is as follows:
，
in the method, in the process of the invention,the amplification factor of the linear vibration pickup system is the ratio of the response amplitude to the input excitation amplitude of the vibration pickup system in a steady state;
when the natural frequency of the vibration pickup systemThe amplitude of the vibration pickup system is as follows:
。
4. the method of claim 1, wherein analyzing the frequency band of the hybrid energy collector according to the kinetic model comprises:
When the amplitude of steady state response of the linear vibration pickup system is reduced to the peak value of the responseWhen the frequency is multiplied, the difference between the two corresponding frequency points is defined as the linear frequency bandwidth of the vibration pickup system, wherein the expression for calculating the linear frequency bandwidth is as follows:
，
in the method, in the process of the invention,is linear bandwidth, < >>For peak value corresponding frequency point, < >>Is +.>The time of the time is multiplied by the corresponding frequency point,for damping ratio->For resonance frequency +.>Is a mechanical damping ratio->Is an electromagnetic damping ratio;
the method comprises the steps of respectively squaring and then adding an expression of a nonlinear approximate solution of the vibration pickup system and an expression between the amplitude and the frequency of the nonlinear vibration pickup system under forced vibration to obtain a nonlinear amplitude-frequency relation of the vibration pickup system:
，
in the method, in the process of the invention,for amplitude of vibration pickup system +.>Is nonlinear and>for normalizing frequency, ++>Is the total damping ratio;
solving a nonlinear amplitude-frequency relation of the vibration pickup system to obtain a relation of the amplitude and the frequency of the vibration pickup system:
，
due to damping ratio in hybrid energy collectorFar less than 1, the relation between the amplitude and the frequency of the vibration pickup system is expressed as:
，
，
in the method, in the process of the invention,corresponding frequency of the first amplitude of the steady-state response of the resonant branch, +.>A corresponding frequency that is a second amplitude of the resonant branch steady state response;
From the expression of the corresponding frequency of the first amplitude of the steady-state response of the resonant branch, whenWith maximum value, equation->Establishment;
pair equationSolving to obtain a relation of maximum amplitude, nonlinearity and damping ratio:
，
in the method, in the process of the invention,is the maximum amplitude;
will beSubstituting the expression of the corresponding frequency of the first amplitude of the steady-state response of the resonance branch into the expression of the corresponding normalized frequency is obtained:
，
in the method, in the process of the invention,is the maximum frequency;
according toDeducing the amplitude drop to +.>Expression of amplitude at time:
，
in the method, in the process of the invention,to decrease to +.>Amplitude at time;
reducing the amplitude toThe amplitude at that time is substituted into the expression of the corresponding frequency of the first amplitude of the steady-state response of the resonant branch to obtain +.>Wherein ∈0 is calculated>The expression of (2) is:
，
in the method, in the process of the invention,to decrease to +.>Frequency at time;
according toAnd->Determining a nonlinear frequency bandwidth of a vibration pickup system in a hybrid energy collector, wherein an expression for calculating the nonlinear frequency bandwidth is:
，
in the method, in the process of the invention,is a nonlinear frequency bandwidth.
5. The method for expanding the frequency band of the hybrid energy collector according to claim 1, wherein the training the uncertain hybrid energy collector model by the data samples generated by finite element simulation to obtain the size parameters of each stage of vibration pickup structure of the hybrid energy collector comprises:
Generating a data sample by finite element software;
dividing each data sample into m-1 signal segments and passing through a convolution layerProjecting each signal segment to a dimension +.>Thereby obtaining a mark embedded layer, wherein +_>For embedding signal sections, 1 and +.>Respectively representing the number of input channels and the number of output channels, < ->、/>Respectively representing the size of the convolution kernel and the shift step length;
will be a learnable oneThe embedded class is connected to->To obtain the signal section embedding +.>And at->Add an insert->Is to obtain the mark embedding +.>Wherein->，/>For classification errors +.>For a desired classification error;
will beIs sent into an encoder, and the encoder is provided with a plurality of data channels,obtaining hidden features of a sample->Hidden feature->The expression of (2) is:
，
in the method, in the process of the invention,embedding +.>Is a hidden layer of the first layer-1,/a>Conceal layer for layer I->Is a multi-layer sensor>To pay attention to the +.>The layer of the material is formed from a layer,layer normalization for applying attention mechanisms;
extraction ofThe signal segment after the medium transformation is used as a target hidden feature to be classified, and the uncertain hybrid energy collector model is subjected to iterative training based on the classified target hidden feature to obtain the hybrid energy collection Dimensional parameters of each level of vibration pickup structure of the collector, wherein the classification process can be described as:
，
in the method, in the process of the invention,to predict the probability distribution of the tag +.>For the expected classification error at L +.>To indicate that the desired classification error at L is entered into the fully connected layer for processing,/>For->Application->The function is activated.
6. The method of claim 1, wherein solving the structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures of each stage comprises:
the elastic coefficient of the first vibration pickup system is equivalent to the sum of the elastic coefficients of four folding beams with double-end guiding boundary conditions;
in the folding supporting beam, n sections of cross beams are arranged, and n-1 sections of vertical beams are correspondingly arranged, wherein Liang Wanju and torque calculation formulas of each section are as follows:
，
，
，
in the method, in the process of the invention,is the bending moment of the middle cross beam of the folding beam, +.>To balance bending moment around Y-axis +.>Force in the Z-axis direction +.>Numbering the cross beam>For the length of the beam->Is a spring beam variable +.>For the torque of the cross beam->For balancing torque around X-axis +.>Is the bending moment of the vertical beam, is->For (I)>Torque for vertical beam ∈ ->Numbering the vertical beams >For the length of the vertical beam->Bending moment of the vertical beam of n-1 sections;
since the cross-sectional areas of the beams are equal, the moment of inertia of the cross-beam about the y-axis and the vertical beam about the x-axis are equal, i.e；
The total deformation of the whole beam is expressed as:
，
in the method, in the process of the invention,for the torsion factor along the Y-axis, +.>Torsional coefficient of rectangular section, +.>For modulus of elasticity>For shear modulus of elasticity, < >>For the torsion factor along the X-axis, +.>Bending moment of n-1 section vertical beam, < ->Torque for n-2 section vertical beams;
wherein, the expression for calculating the torsion coefficient of the rectangular section is:
，
in the method, in the process of the invention,for thickness (S)>For width (S)>Is the elastic coefficient;
the expression for calculating the shear modulus of elasticity is:
，
in the method, in the process of the invention,is poisson's ratio;
force under forceUnder the action, the expression of the deformation of the beam tail end in the Z-axis direction is as follows:
，
in the method, in the process of the invention,is the deformation in the Z-axis direction +.>Is the elastic coefficient in the Z direction, +.>Is a Z-axis force;
according to the forceUnder the action, the deformation of the beam tail end in the Z-axis direction is calculated to obtain the linear elastic coefficient +.>Wherein the linear spring rate in the first vibration pickup system is calculated +.>The expression of (2) is:
，
the supporting beam in the second vibration pickup system is regarded as a folding beam with equal vertical beam length, and in the U-shaped supporting beam, the calculation formulas of bending moment and torque of each section of beam are as follows:
，
，
，
，
In the method, in the process of the invention,to balance bending moment around Y-axis +.>Is the torque at s 1->Is the bending moment of the beam at the first position 1;
the expression of the total deformation of the whole U-shaped supporting beam is as follows:
，
in the method, in the process of the invention,is the bending moment of the beam at the s1 position, < ->For the Y-axis torsion coefficient at s1, < ->For a Y-axis torsion coefficient at s3, < >>For the torsion factor about the X-axis at s3, < >>Is s2 bending moment>For s2 beam torque,/->For the Y-axis torsion coefficient at s2, < ->For the torsion factor about the X-axis at s2, < >>Is the bending moment of the nth section beam, and is->For the nth torque, +.>For the torsion factor about the X-axis at l1, < >>For the Y-axis torsion coefficient at l1, < ->Is the bending moment of the beam at the position l 2->For the beam torque at l1,/->For the torsion factor about the X-axis at l2, < >>A twist factor about the Y axis at l 2;
wherein,，
，
，
，
deriving linear elastic coefficient in second vibration pickup systemThe method comprises the following steps:
，
in the method, in the process of the invention,for n-segment shear modulus of elasticity, < >>Width of girder>Is the length of the vertical beam;
dividing the end of the support beam in the third vibration pickup system into torque around the X-axisAnd bending moment of Y-axis>The bending moment and the torque of each section of beam are respectively as follows:
，
in the method, in the process of the invention,for torque at>Torque at s>Bending moment at position I, I>Is the bending moment of the Y axis, +.>S is the bending moment>A bending moment at c;
deriving linear elastic coefficient in third vibration pickup system by the same method The expression of (2) is:
，
in the method, in the process of the invention,for modulus of elasticity at s>For modulus of elasticity at c>For the shear modulus at c, +.>For the shear modulus at l, +.>For modulus of elasticity at l, +.>For modulus of elasticity at t>For the shear modulus at t>Shear modulus at s, +.>For the modulus of elasticity at r>For modulus of elasticity at i>The shear modulus of elasticity at i.
7. A hybrid energy harvester band expansion system, comprising:
the system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is configured to construct a dynamic model of a hybrid energy collector according to frequency distribution during wheel track vibration, and analyze the frequency band of the hybrid energy collector according to the dynamic model, wherein the hybrid energy collector comprises a first vibration pickup system, a second vibration pickup system and a third vibration pickup system;
a second construction module configured to introduce uncertainty into the attention weight of the kinetic model according to bayesian variation learning, constructing an uncertainty hybrid energy collector model;
the training module is configured to train the uncertain hybrid energy collector model through a data sample generated by finite element simulation to obtain the size parameters of each level of vibration pickup structure of the hybrid energy collector;
And the solving module is configured to solve the structural parameters of the uncertain hybrid energy collector model according to the dimensional parameters of the vibration pickup structures at all levels, and simulate each vibration pickup system part in the hybrid energy collector according to the solving result to obtain the resonance frequency of each vibration pickup system after frequency expansion.
8. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any one of claims 1 to 6.
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