CN116779076A - Super-structure micro-primitive construction method based on numerical calculation - Google Patents
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Abstract
The invention discloses a method for constructing an ultra-structure micro-primitive based on numerical calculation, which belongs to the technical field of metamaterial and comprises the following steps: s1: acquiring the super-structure micro-primitive data in real time, comprehensively processing the super-structure micro-primitive data, and determining the super-structure micro-primitive characteristic data; s2: determining an analysis evaluation table based on the super-structure micro-elements; s3: determining a super-structure micro-element construction strategy, constructing a chiral lattice structure according to the super-structure micro-element construction strategy, determining the super-structure micro-element, and researching the characteristics of the super-structure micro-element by utilizing numerical calculation and combination test. The invention solves the problem that the effective control and isolation of the low-frequency elastic wave can not be realized by a smaller size and the isolation control effect is poor in the prior art, and can realize the super mechanical property which is not possessed by the conventional material by utilizing the special atomic microstructure unit design, and can realize the effective control and isolation of the low-frequency elastic wave by a smaller size and promote the isolation control effect.
Description
Technical Field
The invention relates to the technical field of metamaterials, in particular to a method for constructing a microstructure micro-primitive based on numerical calculation.
Background
The traditional vibration isolation measures can cause poor damping dissipation capability, and larger mass is often needed for realizing control of low-frequency waves, so that the key problem of low-frequency vibration is how to realize coordination and unification of high rigidity and high damping characteristics, and the essence of the method is to research propagation characteristics and regulation rules of elastic waves in a structure.
Chinese patent publication No. CN102810751B discloses a metamaterial and a metamaterial antenna, wherein the metamaterial is relatively arranged in the electromagnetic wave propagation direction of the radiation source; an included angle between a connecting line of the radiation source and a point on the first surface of the metamaterial and a straight line perpendicular to the metamaterial is set as theta, the included angle theta uniquely corresponds to a curved surface in the metamaterial, the refractive indexes of each part on the curved surface uniquely corresponding to the included angle theta are the same, and a generatrix of the curved surface is an elliptical arc; the refractive index of the metamaterial gradually decreases along with the increase of the included angle theta; electromagnetic waves are emitted in parallel on the top surface of each torus after passing through the metamaterial. The jump of the refractive index of the metamaterial is designed to be curved, so that the refraction, diffraction and reflection effects at the jump position are greatly reduced, the problem caused by mutual interference is solved, and the metamaterial antenna have more excellent performance.
The Chinese patent with the publication number of CN103682652B discloses a metamaterial, a preparation method thereof, a metamaterial satellite antenna and a satellite receiving system, wherein the metamaterial comprises a glass fiber reinforced plastic first support body, a glass fiber reinforced plastic second support body and a metamaterial working layer arranged between the glass fiber reinforced plastic first support body and the glass fiber reinforced plastic second support body, the metamaterial working layer comprises at least one metamaterial working sheet layer, the metamaterial working sheet layer comprises a substrate and a metal microstructure attached to the surface of the substrate, and the substrate is a flexible substrate; the metamaterial satellite antenna and the satellite receiving system prepared by the metamaterial are characterized in that the flexible substrate is adopted, and the glass fiber reinforced plastic is used as a support body to prepare the curved surface metamaterial, so that the defect that only a plane metamaterial can be prepared in the prior art is overcome; the curved surface metamaterial can be assembled and connected to prepare a large hollow cylinder metamaterial satellite antenna; the glass fiber reinforced plastic support body can also enable the metamaterial satellite antenna to keep good performance and long service life in an outdoor severe environment. However, the above patent has the following drawbacks in practical use:
effective control and isolation of low-frequency elastic waves cannot be achieved in a small size, and the isolation control effect is poor.
Disclosure of Invention
The invention aims to provide a method for constructing an ultra-structure micro-primitive based on numerical calculation, which can realize the ultra-mechanical property which is not possessed by the conventional material by utilizing the special atomic micro-structure unit design, can realize the effective control and isolation of low-frequency elastic waves with smaller size, improves the isolation control effect, and solves the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the method for constructing the super-structure micro-primitive based on numerical calculation comprises the following steps:
s1: the method comprises the steps of acquiring super-structure micro-primitive data in real time, comprehensively processing the super-structure micro-primitive data, completely extracting the super-structure micro-primitive data from the super-structure micro-primitive data acquired in real time based on the super-structure micro-primitive construction requirement, and searching, sequencing and calculating the super-structure micro-primitive data to determine super-structure micro-primitive characteristic data;
s2: acquiring the characteristic data of the super-structure micro-element, performing association analysis and correlation analysis on the characteristic data of the super-structure micro-element by referring to stored reference experiment standard data with an inertia amplification mechanism based on a data mining technology, and determining an analysis evaluation table based on the super-structure micro-element;
S3: and acquiring an analysis evaluation table based on the super-structure micro-primitives, determining a super-structure micro-primitive construction strategy based on the analysis evaluation table, constructing a chiral lattice structure according to the super-structure micro-primitive construction strategy, determining the super-structure micro-primitives, and researching the characteristics of the super-structure micro-primitives by utilizing numerical calculation and combination tests.
Preferably, in the step S1, the following operations are performed on the meta-structure micro primitive data:
obtaining the data of the super-structure micro-primitive;
extracting the data of the super-structure micro-primitive;
based on the construction requirement of the super-structure micro-primitive, the super-structure micro-primitive data is completely extracted from the super-structure micro-primitive data acquired in real time;
searching the completely extracted super-structure micro-primitive data;
based on the construction requirement of the super-structure micro-primitive, retrieving the super-structure micro-primitive data according to a sequential retrieval method;
filtering out the data of the super-structure micro-element which is valuable for the super-structure micro-element construction, and determining the data of the super-structure micro-element which is valuable for the super-structure micro-element construction.
Preferably, in the step S1, the meta-structure micro primitive data is comprehensively processed, and the following operations are further executed:
Acquiring data of the super-structure micro-element which is valuable to the super-structure micro-element construction;
sequencing the obtained data of the super-structure micro-primitives valuable for constructing the super-structure micro-primitives;
based on an internal ordering method, ordering the super-structure micro-primitive data to determine the super-structure micro-primitive data with distribution characteristics;
performing calculation operation on the super-structure micro-primitive data with the distribution characteristics;
and calculating the super-structure micro-primitive data based on arithmetic and logic operation to determine the super-structure micro-primitive characteristic data.
Preferably, in the step S2, the following operations are performed to determine an analysis evaluation table based on the micro-primitives:
obtaining characteristic data of the super-structure micro-primitive;
based on a data mining technology, and referring to stored reference experiment standard data with an inertia amplification mechanism, performing association analysis and correlation analysis on the characteristic data of the microstructure micro-element;
determining an analysis evaluation table based on the microstructure micro-elements;
aiming at the condition that the characteristic data of the super-structure micro-element is in the range of the reference experimental standard data, the analysis and evaluation table based on the super-structure micro-element can isolate the elastic wave transmission;
Aiming at the condition that the characteristic data of the super-structure micro-element is not in the range of the reference experimental standard data, the analysis and evaluation table based on the super-structure micro-element is that the super-structure micro-element cannot isolate elastic wave propagation.
Preferably, in the step S2, with reference to stored reference experimental standard data with an inertial amplification mechanism, correlation analysis and correlation analysis are performed on the characteristic data of the micro-primitive with the super-structure, and the following operations are performed:
obtaining characteristic data of the super-structure micro-primitive;
based on the characteristic data of the super-structure micro-primitive, the engine searches out reference experiment standard data with an inertia amplification mechanism corresponding to the characteristic data of the super-structure micro-primitive;
and acquiring reference experiment standard data, and retrieving the index of the reference experiment standard data to provide a reference basis for analysis and evaluation of the characteristic data of the super-structure micro-primitive.
Preferably, reference experiment standard data are acquired, and index of the reference experiment standard data is extracted, so that reference basis is provided for analysis and evaluation of the characteristic data of the super-structure micro-element, and the following operations are executed:
determining characteristic variables corresponding to the characteristic data of the super-structure micro-primitive contained in the reference experiment standard data extracted by index adjustment and association constraint relations among all the characteristic variables;
Determining a plurality of characteristic variable combinations based on the association constraint relation;
determining the characteristic data combination corresponding to each characteristic variable combination in the characteristic data of the microstructure micro-element;
taking any one of the feature variables in the feature variable combination as an unknown feature variable, substituting all the remaining super-structure micro-primitive feature data except the unknown feature variable in the feature data combination into an association constraint relation corresponding to the corresponding feature variable combination, and determining a standard association constraint value corresponding to the unknown feature variable;
determining a standard constraint value corresponding to each feature variable in the feature variable combination when the feature variable is an unknown variable combination;
calculating the standard correlation degree of the corresponding feature variable combination based on the specific numerical value of the single super-structure micro-primitive feature data in the corresponding feature data combination and the corresponding standard constraint value of all feature variables in the feature variable combination:
i＝1,2,3,……,n
wherein alpha is RE For the standard correlation of the feature variable combination, n is the total number of feature variables in the feature variable combination, cv i Specific numerical value Cv of single super-structure micro-primitive characteristic data of ith characteristic variable in characteristic variable combination in corresponding characteristic data combination 0i MAX for the standard constraint value corresponding to the ith feature variable in the feature variable combination ]To take the maximum value MIN [ []To take the minimum value;
based on the standard correlation degree of all feature variable combinations, calculating an evaluation index value of each association constraint relation:
wherein index is the evaluation index value of the association constraint relation calculated at present, alpha RE·0 For the standard correlation of the currently calculated feature variable combinations, m is the total number of all feature variable combinations, α RE·j Standard correlation for the j-th feature variable combination;
and generating an analysis evaluation table based on the super-structure micro-primitives based on the evaluation index values of all the association constraint relations.
Preferably, in the step S3, an ultra-structure micro primitive construction strategy is determined, and the following operations are executed:
acquiring an analysis evaluation table based on the microstructure micro-elements;
based on a data mining technology, extending into and mining an analysis evaluation table based on the super-structure micro-primitives, and determining a data mining result based on the super-structure micro-primitives;
and determining an ultra-structure micro-primitive construction strategy based on the data mining result.
Preferably, based on the data mining technology, the analysis evaluation table based on the super-structure micro-primitives is stretched into and mined, the data mining result based on the super-structure micro-primitives is determined, and the following operations are executed:
based on the corresponding evaluation index values of all the association constraint relations in the analysis evaluation table, sequencing all the characteristic variable combinations to obtain first mining weights of the characteristic variable combinations;
Constructing a construction requirement compliance determination model based on the compactness of massive super-structure micro-element construction cases and the correspondence of different construction requirements and a machine learning algorithm;
determining the corresponding construction requirement compliance between a mass of super-structure micro-primitive construction cases and the current super-structure micro-primitive construction requirement based on the construction requirement compliance determination model;
taking any one of all feature variable combinations as a current feature variable, and calculating the dependability of the current feature variable according to the construction requirement conformity corresponding between the super-structure micro-element construction case containing the corresponding current feature variable and the current super-structure micro-element construction requirement by the corresponding construction strategy;
determining the corresponding degree of the evidence when each characteristic variable in all the characteristic variable combinations is used as the current characteristic variable;
calculating a second mining weight of the feature variable combination based on the dependability of all feature variables in the feature variable combination;
taking the average value of the first mining weight and the second mining weight of the characteristic variable combination as the total mining weight of the characteristic variable combination;
and taking the characteristic variable combination with the total mining weight exceeding the mining weight threshold value in all the characteristic variable combinations as a data mining result based on the super-structure micro-primitive.
Preferably, in the step S3, a chiral lattice structure is constructed according to a super-structure micro-primitive construction strategy, the super-structure micro-primitive is determined, and the following operations are executed:
obtaining an ultra-structure micro-primitive construction strategy and a chiral lattice structure;
and constructing a chiral lattice structure based on an ultra-structure micro-element construction strategy, and determining the ultra-structure micro-element capable of isolating elastic wave propagation.
Preferably, in the step S3, the following operations are performed by using numerical calculation and combination test to study the characteristics of the super-structure micro-element:
obtaining constructed super-structure micro-primitives;
numerical calculations and experiments are performed on the super-structure micro-elements to determine super-structure micro-element characteristics including, but not limited to, rotational deformation characteristics, specific strength characteristics, designability characteristics, and isolated elastic wave propagation characteristics.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention obtains the super-structure micro-primitive data in real time, carries out comprehensive processing on the super-structure micro-primitive data, completely extracts the super-structure micro-primitive data from the super-structure micro-primitive data obtained in real time based on the super-structure micro-primitive construction requirement, and carries out retrieval, sequencing and calculation on the super-structure micro-primitive data to determine the super-structure micro-primitive characteristic data.
2. According to the invention, through obtaining the characteristic data of the super-structure micro-element, based on a data mining technology and referring to stored reference experiment standard data with an inertia amplification mechanism, correlation analysis and correlation analysis are carried out on the characteristic data of the super-structure micro-element, so that an analysis evaluation table based on the super-structure micro-element is determined.
3. According to the invention, the super-structure micro-element construction strategy is determined based on the analysis evaluation table by acquiring the analysis evaluation table based on the super-structure micro-element, the chiral lattice structure is constructed according to the super-structure micro-element construction strategy, the super-structure micro-element is determined, the super-structure micro-element characteristics are researched by utilizing numerical calculation and combination tests, the super-mechanical characteristics which are not possessed by the conventional material can be realized by utilizing the special atomic micro-structure unit design, the effective control and isolation of the low-frequency elastic wave can be realized in a smaller size, and the isolation control effect is improved.
Drawings
FIG. 1 is a flow chart of the method for constructing the super-structure micro-primitive based on numerical calculation;
fig. 2 is an algorithm diagram of the method for constructing the super-structure micro-primitive based on numerical calculation.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
In order to solve the problem that the effective control and isolation of the low-frequency elastic wave cannot be realized in a smaller size, and the isolation control effect is poor, referring to fig. 1-2, the present embodiment provides the following technical solutions:
the method for constructing the super-structure micro-primitive based on numerical calculation comprises the following steps:
s1: the method comprises the steps of acquiring super-structure micro-primitive data in real time, comprehensively processing the super-structure micro-primitive data, completely extracting the super-structure micro-primitive data from the super-structure micro-primitive data acquired in real time based on the super-structure micro-primitive construction requirement, and searching, sequencing and calculating the super-structure micro-primitive data to determine super-structure micro-primitive characteristic data;
s2: acquiring the characteristic data of the super-structure micro-element, performing association analysis and correlation analysis on the characteristic data of the super-structure micro-element by referring to stored reference experiment standard data with an inertia amplification mechanism based on a data mining technology, and determining an analysis evaluation table based on the super-structure micro-element;
s3: and acquiring an analysis evaluation table based on the super-structure micro-primitives, determining a super-structure micro-primitive construction strategy based on the analysis evaluation table, constructing a chiral lattice structure according to the super-structure micro-primitive construction strategy, determining the super-structure micro-primitives, and researching the characteristics of the super-structure micro-primitives by utilizing numerical calculation and combination tests.
The chiral lattice material has high specific strength and designability due to a special rotational deformation mechanism, can form a local resonance band gap of sub-wavelength in a wider frequency range, effectively isolates the propagation of elastic waves, constructs a super-structure micro-element with an inertia amplification mechanism on the basis of a chiral lattice structure, utilizes numerical calculation and a combination test to research the characteristics of a super-material, can realize the super-mechanical characteristics which are not possessed by the conventional material, can realize the effective control and isolation of low-frequency elastic waves with smaller size, and improves the isolation control effect.
Specifically, the super-structure micro-element data are obtained in real time, comprehensive processing is carried out on the super-structure micro-element data, super-structure micro-element data are completely extracted from the super-structure micro-element data obtained in real time based on the super-structure micro-element construction requirement, the super-structure micro-element data are searched, sequenced and calculated to determine super-structure micro-element characteristic data, the super-structure micro-element characteristic data are subjected to association analysis and correlation analysis based on a data mining technology and stored reference experiment standard data with an inertia amplification mechanism, an analysis evaluation table based on the super-structure micro-element is determined, an super-structure micro-element construction strategy is determined based on the analysis evaluation table, a chiral lattice structure is constructed according to the super-structure micro-element construction strategy, the super-structure micro-element is determined, the super-structure micro-element characteristics are researched by numerical calculation and combination test, the super-mechanical characteristics which are not possessed by conventional materials can be realized by utilizing special atomic micro-structure unit design, effective control and isolation of low-frequency elastic waves can be realized by smaller size, and isolation control effect is improved.
In S1, carrying out comprehensive processing on the super-structure micro-primitive data, and executing the following operations:
obtaining the data of the super-structure micro-primitive;
extracting the data of the super-structure micro-primitive;
based on the construction requirement of the super-structure micro-primitive, the super-structure micro-primitive data is completely extracted from the super-structure micro-primitive data acquired in real time;
searching the completely extracted super-structure micro-primitive data;
based on the construction requirement of the super-structure micro-primitive, retrieving the super-structure micro-primitive data according to a sequential retrieval method;
filtering out the data of the super-structure micro-element which is valuable for the super-structure micro-element construction, and determining the data of the super-structure micro-element which is valuable for the super-structure micro-element construction.
In S1, the super-structure micro-primitive data is comprehensively processed, and the following operations are further executed:
acquiring data of the super-structure micro-element which is valuable to the super-structure micro-element construction;
sequencing the obtained data of the super-structure micro-primitives valuable for constructing the super-structure micro-primitives;
based on an internal ordering method, ordering the super-structure micro-primitive data to determine the super-structure micro-primitive data with distribution characteristics;
performing calculation operation on the super-structure micro-primitive data with the distribution characteristics;
And calculating the super-structure micro-primitive data based on arithmetic and logic operation to determine the super-structure micro-primitive characteristic data.
The method comprises the steps of obtaining super-structure micro-primitive data, extracting the super-structure micro-primitive data, and completely extracting the super-structure micro-primitive data from the super-structure micro-primitive data obtained in real time based on the super-structure micro-primitive construction requirement;
acquiring fully extracted super-structure micro-element data, performing retrieval operation on the fully extracted super-structure micro-element data, retrieving the super-structure micro-element data according to a sequential retrieval method based on super-structure micro-element construction requirements, filtering out super-structure micro-element data which are valuable for super-structure micro-element construction, and determining the super-structure micro-element data which are valuable for super-structure micro-element construction;
acquiring the data of the super-structure micro-element with value constructed on the super-structure micro-element, sequencing the acquired data of the super-structure micro-element with value constructed on the super-structure micro-element, sequencing the data of the super-structure micro-element based on an internal sequencing method, and determining the data of the super-structure micro-element with distribution characteristics;
and obtaining the super-structure micro-primitive data with the distribution characteristics, performing calculation operation on the super-structure micro-primitive data with the distribution characteristics, and calculating the super-structure micro-primitive data based on arithmetic and logic operation to determine the super-structure micro-primitive characteristic data.
S2, determining an analysis evaluation table based on the super-structure micro-element, and executing the following operations:
obtaining characteristic data of the super-structure micro-primitive;
based on a data mining technology, and referring to stored reference experiment standard data with an inertia amplification mechanism, performing association analysis and correlation analysis on the characteristic data of the microstructure micro-element;
determining an analysis evaluation table based on the microstructure micro-elements;
aiming at the condition that the characteristic data of the super-structure micro-element is in the range of the reference experimental standard data, the analysis and evaluation table based on the super-structure micro-element can isolate the elastic wave transmission;
aiming at the condition that the characteristic data of the super-structure micro-element is not in the range of the reference experimental standard data, the analysis and evaluation table based on the super-structure micro-element is that the super-structure micro-element cannot isolate elastic wave propagation.
S2, referring to stored reference experiment standard data with an inertia amplification mechanism, carrying out association analysis and correlation analysis on the characteristic data of the super-structure micro-element, and executing the following operations:
obtaining characteristic data of the super-structure micro-primitive;
based on the characteristic data of the super-structure micro-primitive, the engine searches out reference experiment standard data with an inertia amplification mechanism corresponding to the characteristic data of the super-structure micro-primitive;
And acquiring reference experiment standard data, and retrieving the index of the reference experiment standard data to provide a reference basis for analysis and evaluation of the characteristic data of the super-structure micro-primitive.
The method includes the steps that super-structure micro-element characteristic data are acquired, based on the super-structure micro-element characteristic data, an engine searches out reference experiment standard data with an inertia amplification mechanism corresponding to the super-structure micro-element characteristic data, acquires the reference experiment standard data, and index-retrieves the reference experiment standard data, and based on a data mining technology, and performs association analysis and correlation analysis on the super-structure micro-element characteristic data by referring to the stored reference experiment standard data with the inertia amplification mechanism, so that an analysis evaluation table based on the super-structure micro-elements is determined.
Acquiring reference experiment standard data, and retrieving reference experiment standard data indexes to provide reference basis for analysis and evaluation of the characteristic data of the super-structure micro-primitive, and executing the following operations:
determining characteristic variables corresponding to the characteristic data of the microstructure micro-element (the characteristic variables are characteristic names corresponding to the characteristic data, such as the migration of manganese element or the change characteristic of leaching amount) and association constraint relations among all the characteristic variables (namely numerical constraint relations among the characteristic variables determined according to the reference experiment standard data) contained in the reference experiment standard data;
Determining a plurality of characteristic variable combinations (namely combinations formed by characteristic variables with association constraint relations) based on the association constraint relations;
and determining a characteristic data combination corresponding to each characteristic variable combination (namely, a combination formed by specific values corresponding to the characteristic variables contained in the characteristic variable combination in the characteristic data of the microstructure micro-element) from the characteristic data of the microstructure micro-element;
any feature variable in the feature variable combination is taken as an unknown feature variable, all the remaining super-structure micro-primitive feature data except the unknown feature variable in the feature data combination are substituted into the association constraint relation corresponding to the corresponding feature variable combination, and the standard association constraint value of the corresponding unknown feature variable is determined (because the association constraint relation can be expressed by a mathematical relation, the specific value of the feature data corresponding to the feature variable (namely the corresponding feature variable combination) which is related in the association constraint relation and is the remaining feature variable except the unknown feature variable is substituted into the association constraint relation, so that the ideal feature value (namely the corresponding standard constraint value) of the unknown feature variable can be obtained, and the ideal feature value enables the feature variables to meet the association constraint relation in the reference experiment standard data);
Determining a standard constraint value corresponding to each feature variable in the feature variable combination when the feature variable is an unknown variable combination;
calculating the standard correlation degree of the corresponding feature variable combination based on the specific numerical value of the single super-structure micro-primitive feature data in the corresponding feature data combination and the corresponding standard constraint value of all feature variables in the feature variable combination:
i＝1,2,3,……,n
wherein alpha is RE For the standard correlation of the feature variable combination, n is the total number of feature variables in the feature variable combination, cv i Specific numerical value Cv of single super-structure micro-primitive characteristic data of ith characteristic variable in characteristic variable combination in corresponding characteristic data combination 0i MAX for the standard constraint value corresponding to the ith feature variable in the feature variable combination]To take the maximum value MIN [ []To take the minimum value;
based on the formula, calculating a numerical value of uniformity degree of deviation values between the single super-structure micro-primitive characteristic data in the characteristic variable combination and ideal characteristic values meeting association constraint relation in reference experiment standard data, namely standard correlation degree, by calculating crossing degree between specific numerical values of all characteristic variables in the characteristic variable combination in the single super-structure micro-primitive characteristic data in the corresponding characteristic data combination and maximum value and minimum value of deviation degree of corresponding standard constraint values;
Based on the standard correlation degree of all feature variable combinations, calculating an evaluation index value of each association constraint relation:
wherein index is the evaluation index value of the association constraint relation calculated at present, alpha RE·0 For the standard correlation of the currently calculated feature variable combinations, m is the total number of all feature variable combinations, α RE·j Standard correlation for the j-th feature variable combination;
based on the formula, an evaluation index value which can represent the association constraint relation of the microstructure micro-primitive characteristic data relative to the current calculation can be calculated by calculating the similarity between the standard correlation of each characteristic variable combination and the average value of the standard correlation of all characteristic variable combinations;
the formula is characterized in that the higher the similarity is, the higher the evaluation index value of the association constraint relation of the characteristic data of the super-structure micro-primitives relative to the current calculation is, so that the evaluation index value is conveniently determined to further determine the super-structure micro-primitive construction strategy.
Generating an analysis evaluation table based on the super-structure micro-primitive based on the evaluation index values of all the association constraint relations (the analysis evaluation table is a table containing the evaluation index values of all the association constraint relations);
based on all the steps, the following steps are realized: the relation constraint of the characteristic variables is realized through the characteristic variables and the association constraint relation among the characteristic variables contained in the reference experiment standard data which are extracted through index adjustment, the standard correlation degree of the characteristic variable combination is calculated, the evaluation index value of the association constraint relation is further calculated, and the evaluation index value is obtained from the correlation degree between the numerical relation among specific characteristic values of different characteristic variables in the super-structure micro-primitive characteristic data and the association constraint relation under the ideal state contained in the reference experiment standard data, so that the variable evaluation of the super-structure micro-primitive characteristic data is realized.
S3, determining an ultra-structure micro-primitive construction strategy, and executing the following operations:
acquiring an analysis evaluation table based on the microstructure micro-elements;
based on a data mining technology, extending into and mining an analysis evaluation table based on the super-structure micro-primitives, and determining a data mining result based on the super-structure micro-primitives;
and determining an ultra-structure micro-primitive construction strategy based on the data mining result.
Based on a data mining technology, the analysis evaluation table based on the super-structure micro-primitives is stretched into mining, the data mining result based on the super-structure micro-primitives is determined, and the following operations are executed:
sequencing all feature variable combinations based on the corresponding evaluation index values of all the association constraint relations in the analysis evaluation table to obtain a first mining weight of the feature variable combinations (the first mining weight characterizes the selectable degree (or weight value) of the feature variable in the feature variable combinations determined based on the evaluation index values, which can be used as the basis of a subsequent construction strategy);
constructing a construction requirement compliance determination model based on the compactness corresponding to the massive super-structure micro-element construction cases and different construction requirements and a machine learning algorithm (the specific implementation steps of the construction model process comprise tightly performing machine learning on the massive super-structure micro-element construction cases and the corresponding different construction requirements based on the machine learning algorithm, so as to construct a construction requirement compliance determination model, wherein the construction requirement compliance determination model can identify the construction requirement compliance between the different super-structure micro-element construction cases and the different super-structure micro-element construction requirements, and the construction requirement compliance is the compliance between the construction requirements actually met by the corresponding super-structure micro-element construction cases and the corresponding super-structure micro-element construction requirements;
Determining the corresponding construction requirement compliance between a massive super-structure micro-element construction case and the current super-structure micro-element construction requirement based on the construction requirement compliance determination model (namely, inputting each super-structure micro-element construction case and the current super-structure micro-element construction requirement into the construction requirement compliance determination model to determine the corresponding construction requirement compliance), wherein the massive super-structure micro-element construction case adopted in the step can be consistent or inconsistent with the massive super-structure micro-element construction case adopted in the model construction process, and the execution of the step is not influenced);
taking any feature variable in all feature variable combinations as a current feature variable, and calculating the dependability of the current feature variable according to the corresponding construction requirement conformity between the super-structure micro-element construction case containing the corresponding current feature variable and the current super-structure micro-element construction requirement by using the corresponding construction strategy (the calculation process is to take the construction strategy as the sum of the corresponding construction requirement conformity between all super-structure micro-element construction cases containing the corresponding current feature variable and the current super-structure micro-element construction requirement, and taking the dependability as the dependability of the current feature variable, namely representing the degree value when the current super-structure micro-element construction requirement can determine the corresponding construction strategy according to the feature variable);
Determining the corresponding degree of the evidence when each characteristic variable in all the characteristic variable combinations is used as the current characteristic variable;
calculating a second mining weight of the feature variable combination based on the dependability of all feature variables in the feature variable combination (the calculation process is that an average value of the dependability of all feature variables in the feature variable combination is taken as the second mining weight of the feature variable combination, and the second mining weight is that the feature variable in the feature variable combination determined based on the dependability of all feature variables in the feature variable combination can be used as an optional degree (or weight value) of the basis of a subsequent construction strategy);
taking the average value of the first mining weight and the second mining weight of the feature variable combination as the total mining weight of the feature variable combination (the total mining weight is the selectable degree (or weight value) which characterizes the basis of the evaluation index value and all feature variables in the feature variable combination and can be used as the basis of the subsequent construction strategy);
and the feature variable combination with the total mining weight exceeding the mining weight threshold value (the mining weight threshold value is a screening threshold value used for screening out the total mining weight which can be used as the basis for constructing the strategy in the feature variable combination) in all the feature variable combinations is used as the data mining result based on the super-structure micro-primitive;
Based on all the steps, the following steps are realized: the evaluation index value in the characteristic variable combination in the analysis evaluation table is combined, the degree of the corresponding construction strategy can be determined according to the characteristic variable by further calculating the construction requirement of the characteristic current super-structure micro-element, further mining of the characteristic variable combination in the analysis evaluation table is realized, and a basis is provided for determining the follow-up construction strategy.
S3, constructing a chiral lattice structure according to a super-structure micro-element construction strategy, determining a super-structure micro-element, and executing the following operations:
obtaining an ultra-structure micro-primitive construction strategy and a chiral lattice structure;
and constructing a chiral lattice structure based on an ultra-structure micro-element construction strategy, and determining the ultra-structure micro-element capable of isolating elastic wave propagation.
S3, researching the characteristics of the super-structure micro-element by utilizing numerical calculation and combination test, and executing the following operations:
obtaining constructed super-structure micro-primitives;
numerical calculations and experiments are performed on the super-structure micro-elements to determine super-structure micro-element characteristics including, but not limited to, rotational deformation characteristics, specific strength characteristics, designability characteristics, and isolated elastic wave propagation characteristics.
The method comprises the steps of obtaining an analysis evaluation table based on the super-structure micro-element, carrying out extension mining on the analysis evaluation table based on the super-structure micro-element based on a data mining technology, determining a data mining result based on the super-structure micro-element, determining a super-structure micro-element construction strategy based on the data mining result, obtaining the super-structure micro-element construction strategy and a chiral lattice structure, constructing the chiral lattice structure based on the super-structure micro-element construction strategy, determining the super-structure micro-element capable of isolating elastic wave propagation, obtaining the constructed super-structure micro-element, carrying out numerical calculation and test on the super-structure micro-element, and determining super-structure micro-element characteristics, wherein the super-structure micro-element characteristics comprise but are not limited to rotation deformation characteristics, specific strength characteristics, designable characteristics and isolation elastic wave propagation characteristics.
In summary, the method for constructing the super-structure micro-primitive based on numerical computation acquires super-structure micro-primitive data in real time, comprehensively processes the super-structure micro-primitive data, completely extracts the super-structure micro-primitive data from the super-structure micro-primitive data acquired in real time based on the super-structure micro-primitive construction requirement, searches, sorts and calculates the super-structure micro-primitive data to determine super-structure micro-primitive characteristic data, performs association analysis and correlation analysis on the super-structure micro-primitive characteristic data based on a data mining technology by referring to stored reference experiment standard data with an inertia amplification mechanism, determines an analysis evaluation table based on the super-structure micro-primitive, determines a super-structure micro-primitive construction strategy based on the analysis evaluation table, constructs a chiral lattice structure according to the super-structure micro-primitive construction strategy, utilizes numerical computation and combination experiments to research super-structure micro-primitive characteristics, and utilizes special atomic micro-structure unit design to realize effective control and isolation of low-frequency elastic waves with smaller size, thereby improving isolation control effect.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The method for constructing the super-structure micro-primitive based on the numerical calculation is characterized by comprising the following steps of:
s1: the method comprises the steps of acquiring super-structure micro-primitive data in real time, comprehensively processing the super-structure micro-primitive data, completely extracting the super-structure micro-primitive data from the super-structure micro-primitive data acquired in real time based on the super-structure micro-primitive construction requirement, and searching, sequencing and calculating the super-structure micro-primitive data to determine super-structure micro-primitive characteristic data;
S2: acquiring the characteristic data of the super-structure micro-element, performing association analysis and correlation analysis on the characteristic data of the super-structure micro-element by referring to stored reference experiment standard data with an inertia amplification mechanism based on a data mining technology, and determining an analysis evaluation table based on the super-structure micro-element;
s3: and acquiring an analysis evaluation table based on the super-structure micro-primitives, determining a super-structure micro-primitive construction strategy based on the analysis evaluation table, constructing a chiral lattice structure according to the super-structure micro-primitive construction strategy, determining the super-structure micro-primitives, and researching the characteristics of the super-structure micro-primitives by utilizing numerical calculation and combination tests.
2. The method for constructing the super-structure micro-primitive based on numerical calculation according to claim 1, wherein the method comprises the following steps: in the step S1, the microstructure micro-primitive data is comprehensively processed, and the following operations are executed:
obtaining the data of the super-structure micro-primitive;
extracting the data of the super-structure micro-primitive;
based on the construction requirement of the super-structure micro-primitive, the super-structure micro-primitive data is completely extracted from the super-structure micro-primitive data acquired in real time;
searching the completely extracted super-structure micro-primitive data;
Based on the construction requirement of the super-structure micro-primitive, retrieving the super-structure micro-primitive data according to a sequential retrieval method;
filtering out the data of the super-structure micro-element which is valuable for the super-structure micro-element construction, and determining the data of the super-structure micro-element which is valuable for the super-structure micro-element construction.
3. The method for constructing the super-structure micro-primitive based on numerical calculation according to claim 2, wherein the method comprises the following steps: in the step S1, the microstructure micro-primitive data is comprehensively processed, and the following operations are further executed:
acquiring data of the super-structure micro-element which is valuable to the super-structure micro-element construction;
sequencing the obtained data of the super-structure micro-primitives valuable for constructing the super-structure micro-primitives;
based on an internal ordering method, ordering the super-structure micro-primitive data to determine the super-structure micro-primitive data with distribution characteristics;
performing calculation operation on the super-structure micro-primitive data with the distribution characteristics;
and calculating the super-structure micro-primitive data based on arithmetic and logic operation to determine the super-structure micro-primitive characteristic data.
4. The method for constructing the super-structure micro-primitive based on numerical computation according to claim 3, wherein the method comprises the following steps: in the step S2, determining an analysis evaluation table based on the super-structure micro-primitive, and executing the following operations:
Obtaining characteristic data of the super-structure micro-primitive;
based on a data mining technology, and referring to stored reference experiment standard data with an inertia amplification mechanism, performing association analysis and correlation analysis on the characteristic data of the microstructure micro-element;
determining an analysis evaluation table based on the microstructure micro-elements;
aiming at the condition that the characteristic data of the super-structure micro-element is in the range of the reference experimental standard data, the analysis and evaluation table based on the super-structure micro-element can isolate the elastic wave transmission;
aiming at the condition that the characteristic data of the super-structure micro-element is not in the range of the reference experimental standard data, the analysis and evaluation table based on the super-structure micro-element is that the super-structure micro-element cannot isolate elastic wave propagation.
5. The method for constructing the super-structure micro-primitive based on numerical computation according to claim 4, wherein the method comprises the following steps: in the step S2, with reference to stored reference experimental standard data with an inertial amplification mechanism, performing association analysis and correlation analysis on the characteristic data of the microstructure micro-element, and performing the following operations:
obtaining characteristic data of the super-structure micro-primitive;
based on the characteristic data of the super-structure micro-primitive, the engine searches out reference experiment standard data with an inertia amplification mechanism corresponding to the characteristic data of the super-structure micro-primitive;
And acquiring reference experiment standard data, and retrieving the index of the reference experiment standard data to provide a reference basis for analysis and evaluation of the characteristic data of the super-structure micro-primitive.
6. The method for constructing the super-structure micro-primitive based on numerical computation according to claim 5, wherein the method comprises the following steps: acquiring reference experiment standard data, and retrieving reference experiment standard data indexes to provide reference basis for analysis and evaluation of the characteristic data of the super-structure micro-primitive, and executing the following operations:
determining characteristic variables corresponding to the characteristic data of the super-structure micro-primitive contained in the reference experiment standard data extracted by index adjustment and association constraint relations among all the characteristic variables;
determining a plurality of characteristic variable combinations based on the association constraint relation;
determining the characteristic data combination corresponding to each characteristic variable combination in the characteristic data of the microstructure micro-element;
taking any one of the feature variables in the feature variable combination as an unknown feature variable, substituting all the remaining super-structure micro-primitive feature data except the unknown feature variable in the feature data combination into an association constraint relation corresponding to the corresponding feature variable combination, and determining a standard association constraint value corresponding to the unknown feature variable;
Determining a standard constraint value corresponding to each feature variable in the feature variable combination when the feature variable is an unknown variable combination;
calculating the standard correlation degree of the corresponding feature variable combination based on the specific numerical value of the single super-structure micro-primitive feature data in the corresponding feature data combination and the corresponding standard constraint value of all feature variables in the feature variable combination:
i＝1,2,3,……,n
wherein alpha is RE For the standard correlation of the feature variable combination, n is the total number of feature variables in the feature variable combination, cv i Specific numerical value Cv of single super-structure micro-primitive characteristic data of ith characteristic variable in characteristic variable combination in corresponding characteristic data combination 0i MAX for the standard constraint value corresponding to the ith feature variable in the feature variable combination]To take the maximum value MIN [ []To take the minimum value;
based on the standard correlation degree of all feature variable combinations, calculating an evaluation index value of each association constraint relation:
wherein index is the evaluation index value of the association constraint relation calculated at present, alpha RE·0 For the standard correlation of the currently calculated feature variable combinations, m is the total number of all feature variable combinations, α RE·j Standard correlation for the j-th feature variable combination;
and generating an analysis evaluation table based on the super-structure micro-primitives based on the evaluation index values of all the association constraint relations.
7. The method for constructing the super-structure micro-primitive based on numerical computation according to claim 6, wherein the method comprises the following steps: in the step S3, determining an ultra-structure micro primitive construction strategy, and executing the following operations:
acquiring an analysis evaluation table based on the microstructure micro-elements;
based on a data mining technology, extending into and mining an analysis evaluation table based on the super-structure micro-primitives, and determining a data mining result based on the super-structure micro-primitives;
and determining an ultra-structure micro-primitive construction strategy based on the data mining result.
8. The method for constructing the super-structure micro-primitive based on numerical calculation according to claim 7, wherein the method comprises the following steps: based on a data mining technology, the analysis evaluation table based on the super-structure micro-primitives is stretched into mining, the data mining result based on the super-structure micro-primitives is determined, and the following operations are executed:
based on the corresponding evaluation index values of all the association constraint relations in the analysis evaluation table, sequencing all the characteristic variable combinations to obtain first mining weights of the characteristic variable combinations;
constructing a construction requirement compliance determination model based on the compactness of massive super-structure micro-element construction cases and the correspondence of different construction requirements and a machine learning algorithm;
Determining the corresponding construction requirement compliance between a mass of super-structure micro-primitive construction cases and the current super-structure micro-primitive construction requirement based on the construction requirement compliance determination model;
taking any one of all feature variable combinations as a current feature variable, and calculating the dependability of the current feature variable according to the construction requirement conformity corresponding between the super-structure micro-element construction case containing the corresponding current feature variable and the current super-structure micro-element construction requirement by the corresponding construction strategy;
determining the corresponding degree of the evidence when each characteristic variable in all the characteristic variable combinations is used as the current characteristic variable;
calculating a second mining weight of the feature variable combination based on the dependability of all feature variables in the feature variable combination;
taking the average value of the first mining weight and the second mining weight of the characteristic variable combination as the total mining weight of the characteristic variable combination;
and taking the characteristic variable combination with the total mining weight exceeding the mining weight threshold value in all the characteristic variable combinations as a data mining result based on the super-structure micro-primitive.
9. The method for constructing the super-structure micro-primitive based on numerical calculation according to claim 8, wherein the method comprises the following steps: in the step S3, a chiral lattice structure is constructed according to a super-structure micro-primitive construction strategy, the super-structure micro-primitive is determined, and the following operations are executed:
Obtaining an ultra-structure micro-primitive construction strategy and a chiral lattice structure;
and constructing a chiral lattice structure based on an ultra-structure micro-element construction strategy, and determining the ultra-structure micro-element capable of isolating elastic wave propagation.
10. The method for constructing the super-structure micro-primitive based on numerical calculation according to claim 9, wherein the method comprises the following steps: in the step S3, the characteristics of the super-structure micro-primitive are researched by utilizing numerical calculation and combination test, and the following operations are executed:
obtaining constructed super-structure micro-primitives;
numerical calculations and experiments are performed on the super-structure micro-elements to determine super-structure micro-element characteristics including, but not limited to, rotational deformation characteristics, specific strength characteristics, designability characteristics, and isolated elastic wave propagation characteristics.
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