CN113053475B - Signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis - Google Patents
Signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis Download PDFInfo
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Abstract
The invention relates to a signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis, which comprises the steps of compressing a resonance frequency change curve, and calibrating a baseline point of each concentration on the compressed curve; performing piecewise linear fitting on a baseline according to the baseline points of each concentration, and deducting the baseline by using the initial resonance change frequency curve to obtain a standard resonance frequency change curve; calculating characteristic parameters of the gas-sensitive material according to the standard resonance frequency change curve; selecting typical gas-sensitive material parameters to construct a gas-sensitive material evaluation decision model, and performing multi-attribute decision on the gas-sensitive material. The method for performing baseline point automatic calibration, baseline correction and multi-attribute decision on the resonance frequency output signal is established, the problem of baseline drift of the output signal is solved, meanwhile, the automatic calculation of the parameters of the gas sensitive material is realized, the experimental efficiency of the gas sensitive material can be greatly improved, accidental errors are reduced, and the multi-attribute decision on the gas sensitive material is realized by constructing a gas sensitive material evaluation decision model.
Description
Technical Field
The invention relates to the technical field of gas-sensitive material testing, in particular to a signal processing and multi-attribute decision method based on micro-cantilever gas-sensitive material analysis.
Background
The gas-sensitive material analyzer is an innovative scientific research instrument for rapidly detecting the action of a gas-sensitive material on a trace gas molecular interface on line, takes a resonant micro-cantilever as a biochemical detection device, takes a variable-temperature micro-weighing method as a test theory basis to quantitatively extract characteristic parameters of the gas-sensitive material, starts from the characteristic parameters of adsorption and desorption thermodynamics and kinetics, provides a theoretical basis for the structure optimization of the material, and has the characteristics of higher accuracy, stability and efficiency compared with the traditional scientific research instrument. The gas sensitive material analyzer outputs a resonance frequency change curve, and quantitatively calculates characteristic parameters of the material from the theoretical angle of thermodynamics and kinetics, but the output signal of the gas sensitive material analyzer has the problem of baseline drift, which can cause larger deviation in calculation of related characteristic parameters. The problems of low efficiency, accidental errors and the like exist in the conventional manual parameter calculation, and a set of complete automatic output signal processing and multi-attribute decision-making system is lacked in the calculation and analysis process.
In the prior art, in the aspect of baseline correction, most characteristic differences between a baseline and a curve are utilized, a proper separation condition is selected, and a slowly-changed baseline is separated from the curve. Common baseline correction methods include differentiation, wavelet transformation, morphological methods based on curve characteristics, polynomial fitting, and piecewise linear fitting. The baseline correction method has a good baseline correction effect when dealing with single experimental concentration, but when the gas-sensitive material has more experimental concentrations of adsorption and desorption, a complete characteristic peak is difficult to eliminate from a slowly-changed baseline, so that the baseline cannot be accurately obtained, and the baseline correction effect is influenced.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the defect that the prior art lacks an automatic output signal processing and multi-attribute decision method in the calculation and analysis process.
In order to solve the above technical problems, an object of the present invention is to provide a signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis, comprising:
compressing the resonance frequency change curve, and automatically calibrating the baseline base line point of each concentration on the compressed curve;
fitting a baseline according to the baseline points of each concentration by a piecewise linear baseline point, and deducting the baseline by using an initial resonance frequency change curve to obtain a standard resonance frequency change curve;
calculating characteristic parameters of the gas-sensitive material according to the standard resonance frequency change curve;
selecting characteristic parameters of a typical gas sensitive material to construct a gas sensitive material evaluation decision model, and performing multi-attribute decision on the gas sensitive material.
In one embodiment of the present invention, compressing the resonant frequency variation curve, and automatically calibrating the baseline points of each concentration on the compressed curve comprises:
s11: obtaining a resonance frequency change curve, and smoothing the resonance frequency change curve;
s12: compressing the curve after the smoothing treatment by adopting a D-P algorithm;
s13: the baseline points for each concentration are calibrated on the curve after compression.
In an embodiment of the present invention, compressing the smoothed curve by using the D-P algorithm includes:
s121: connecting two end points A, B of the curve into a straight line AB;
s122: calculating the distance from each point on the curve to the straight line AB to obtain a point C with the maximum distance;
s123: comparing the distance from the point C to the straight line AB with a preset threshold value of a D-P algorithm, and when the distance from the point C to the straight line AB is less than or equal to the threshold value, representing the section of curve by using the straight line AB;
s124: when the distance from the point C to the straight line AB is greater than the threshold value, the point C is used as a separation point to divide the curve, and the processing from S121 to S123 is repeated on the curve segments AC and BC;
s125: after the processing of all the curve segments is finished, the broken line segments connecting all the separation points approximately represent the curve.
In one embodiment of the present invention, calibrating the baseline points for each concentration on the compressed curve comprises:
s131: solving a local minimum value of the compressed curve to obtain a reaction equilibrium point with corresponding concentration;
s132: taking the position of the reaction equilibrium point as a reference coordinate, and acquiring an adsorption starting point according to the slope characteristics;
s133: based on DTW algorithm, the next concentration adsorption starting point is matched with the current concentration reaction equilibrium point in a reverse sequence and subsection manner, the curve section with the maximum similarity is searched, and the desorption cutoff point is obtained.
In one embodiment of the invention, the gas sensitive material parameters include thermodynamic parameters including enthalpy change, entropy change, and gibbs free energy change, and kinetic parameters including adsorption/desorption rate constant, total active site number, coverage, equilibrium constant, and activation energy.
In one embodiment of the invention, the gas sensitive material evaluation decision model comprises at least a target layer, a criterion layer and a scheme layer, wherein the criterion layer comprises a plurality of evaluation attributes, and the scheme layer comprises a candidate decision material set.
In one embodiment of the present invention, making a multi-attribute decision on a gas sensitive material comprises:
performing multi-attribute decision on the gas sensitive material by using a VIKOR method, wherein the VIKOR adopts the following L in the decision p -metric aggregation function:
in the formula, p is more than or equal to 1 and less than or equal to infinity, J =1,2, …, J, variable J represents the number of decision-making materials to be selected, and each decision-making material to be selected uses a j Is shown as f ij Representing candidate decision material a j Measure L of the property value of the ith criterion p,j Representation scheme a j Distance to ideal solution, f i * Positive ideal solution, f, representing the ith criterion attribute i - Negative ideal solution, w, representing the ith criterion attribute i Representing the weight of the ith criterion attribute.
In one embodiment of the invention, the multi-attribute decision making on the gas sensitive material by using the VIKOR method comprises the following steps:
standardizing the attribute values of the decision-making materials to be selected, determining the weight of each attribute based on an improved entropy weight method, and determining a positive ideal solution and a negative ideal solution of each attribute;
calculating the group utility value and the individual regret value of the comprehensive evaluation of each decision material to be selected according to the positive ideal solution and the negative ideal solution;
calculating benefit ratio values generated by the decision-making materials to be selected according to the group utility values and the individual regret values, and determining the arrangement sequence of the decision-making materials to be selected according to the group utility values, the individual regret values and the benefit ratio values;
and determining a compromise scheme according to the arrangement sequence of the decision-making materials to be selected.
Another objective of the present invention is to provide a signal processing and multi-attribute decision making system based on micro-cantilever gas sensitive material analysis, comprising: the base line point automatic calibration module is used for compressing the resonance frequency change curve, and the base line point of the base line point automatic calibration module is used for automatically calibrating the base line point of each concentration on the compressed curve;
the baseline correction module is used for piecewise linearly fitting a baseline according to the baseline point of each concentration and deducting the baseline by using an initial resonance frequency change curve to obtain a standard resonance frequency change curve;
the parameter calculation module is used for calculating characteristic parameters of the gas-sensitive material according to the standard resonance frequency change curve;
and the multi-attribute decision module is used for selecting characteristic parameters of a typical gas sensitive material to construct a gas sensitive material evaluation decision model and performing multi-attribute decision on the gas sensitive material. Compared with the prior art, the technical scheme of the invention has the following advantages:
the method is based on the micro-cantilever resonance technology, combines a variable-temperature micro-weighing method to establish a method for automatically calibrating the baseline point of the resonance frequency output signal, correcting the baseline and making a multi-attribute decision, solves the problem of baseline drift of the output signal, simultaneously realizes the automatic calculation of the parameters of the gas sensitive material, can greatly improve the experimental efficiency of the gas sensitive material and reduce accidental errors, and realizes the multi-attribute decision of the gas sensitive material by establishing a gas sensitive material evaluation decision model, and can select the optimal gas sensitive material according to different application occasions.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which
FIG. 1 is a schematic diagram of the interaction of the gas sensitive material of the present invention with trace gas molecules.
Fig. 2 is a block diagram showing the structure of the gas sensitive material analyzing system of the present invention.
FIG. 3 is a diagram illustrating a variation curve of resonant frequency according to the present invention.
Fig. 4 is a schematic flow chart of a signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis according to an embodiment of the present invention.
Fig. 5 is a schematic flow chart of S1 in fig. 4.
FIG. 6 is a diagram illustrating the D-P algorithm according to an embodiment of the present invention.
Fig. 7 is a schematic flow chart of solving the desorption cutoff point in the first embodiment of the present invention.
FIG. 8 is a schematic diagram of the calculation process of thermodynamic and kinetic parameters of the adsorption and desorption reactions of the gas-sensitive material in the first embodiment of the present invention.
FIG. 9 is a model for evaluating and deciding the optimal solution of the gas-sensitive material according to the first embodiment of the present invention.
FIG. 10 is a flowchart illustrating a method for performing multi-attribute decision-making on a gas sensitive material by using a VIKOR method according to an embodiment of the invention.
Fig. 11 is a block diagram of a signal processing and multi-attribute decision making system based on micro-cantilever gas sensitive material analysis according to a second embodiment of the present invention.
Description of reference numerals: 100. a baseline point automatic calibration module; 200. a baseline correction module; 300. a parameter calculation module; 400. and a multi-attribute decision module.
Detailed Description
The present invention is further described below in conjunction with the drawings and the embodiments so that those skilled in the art can better understand the present invention and can carry out the present invention, but the embodiments are not to be construed as limiting the present invention.
In order to better understand the signal processing and multi-attribute decision method based on the micro-cantilever gas sensitive material analysis disclosed by the embodiment of the invention, the principle of the gas sensitive material analyzer disclosed by the invention is firstly explained in detail.
The gas sensitive material analyzer is an innovative scientific research instrument for monitoring the interface action process of an advanced gas sensitive material on trace molecules in an on-line in-situ manner by adopting a resonant micro-cantilever sensor with ultrahigh mass sensitivity. FIG. 1 is a schematic diagram of the interaction between a gas-sensitive material and a trace gas molecule interface.
Referring to fig. 1, the resonant micro-cantilever works in a dynamic mode, the number of adsorbed gas molecules is represented in real time by using the change of resonant frequency, the purpose of detecting characteristic parameters of the gas-sensitive material is achieved, and the quality detection sensitivity can reach 10 -18 g is even lower. The basic test principle is as follows: the surface of the free end of the micro-cantilever beam is coated with a layer of specific gas-sensitive material, the specific gas-sensitive material is placed in a constant temperature cavity, and after gas to be detected is introduced, the layer of gas-sensitive material and gas micromolecules generate adsorption and desorption reaction, so that the mass change of the micro-cantilever beam is caused, and the change of the resonant frequency is further caused.
Fig. 2 shows a gas sensitive material analysis system designed based on the gas sensitive material analyzer, and fig. 2 is a structural block diagram of the gas sensitive material analysis system. Referring to fig. 2, the overall design idea of the system is to combine the micro-cantilever resonance technology, perform a specific adsorption and desorption reaction experiment on an advanced gas-sensitive material by using a gas-sensitive material analyzer to obtain an output resonance frequency change curve, then calculate thermodynamic and kinetic parameters for the specific adsorption and desorption reaction of the gas-sensitive material, select typical parameters, construct a gas-sensitive material evaluation decision model, and complete the multi-attribute decision characteristic evaluation of the gas-sensitive material.
Fig. 3 is a schematic diagram of the output resonant frequency variation curve, in which the frequency curves of two concentrations of gas at the same constant temperature are shown, and the test curve of each concentration includes a preparation phase, an adsorption phase and a desorption phase. And introducing the gas to be detected after the resonant frequency of the micro-cantilever beam is kept stable for a period of time, wherein the position of the adsorption starting point is in the curve. The gas-sensitive material and target gas molecules generate interface reaction to cause the mass of the micro-cantilever beam to increase, so that the resonance frequency is reduced, and the gas-sensitive material does not react with the gas any more when the material is saturated in adsorption and reaches a reaction equilibrium point. At this time, another inert no-load gas (usually high-purity nitrogen) is introduced, so that gas small molecules attached to the gas sensitive material are subjected to desorption reaction, the desorption cut-off point is reached after desorption balance, the resonant frequency of the micro-cantilever beam tends to be stable, and the gas test of the next concentration is repeated after the frequency curve is kept stable for a period of time.
Example one
Fig. 4 is a schematic flow chart of a signal processing and multi-attribute decision making method based on micro-cantilever gas sensitive material analysis according to an embodiment of the present invention.
On the basis of fig. 1 to fig. 3, an embodiment of the present invention provides a signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis, as shown in fig. 4, the method includes the following steps:
s1: and compressing the resonance frequency change curve, and automatically calibrating the baseline point of each concentration on the compressed curve.
For example, referring to fig. 5, compressing the resonant frequency variation curve, and automatically calibrating the baseline point of each concentration on the compressed curve includes the following specific steps:
s11: obtaining a resonance frequency change curve, and smoothing the resonance frequency change curve, wherein the window length of the Gaussian window can be 10.
S12: and compressing the smoothed curve by adopting a D-P algorithm, and completely describing the profile characteristics of the curve while keeping the detail characteristics of the curve, so that the problem of high difficulty in searching a baseline point caused by excessive frequency curve data points is solved, wherein the threshold value of the D-P algorithm can be 0.1. FIG. 6 is a schematic diagram of the D-P algorithm, which includes the following steps: s121) connecting two end points A, B of the curve into a straight line AB; s122) calculating the distance from each point on the curve to the straight line AB to obtain a point C with the maximum distance; s123) comparing the distance from the point C to the straight line AB with a preset threshold value of a D-P algorithm, and when the distance from the point C to the straight line AB is less than or equal to the threshold value, representing the section of curve by using the straight line AB; s124) when the distance from the point C to the straight line AB is larger than a threshold value, the point C is used as a separation point to divide the curve, and the processing from S121 to S123 is repeated on the curve segments AC and BC; s125) after the processing of all the curve segments is finished, the broken line segments formed by connecting all the separation points approximately represent the curve,
s13: the baseline points for each concentration are calibrated on the curve after compression. The characteristic points to be identified are obtained after curve compression, in order to realize piecewise linear fitting of a baseline, three baseline points required by baseline fitting need to be obtained, the three baseline points need to be automatically obtained from all the characteristic points to be identified, wherein the three baseline points are as follows: reaction equilibrium point, adsorption start point and desorption cut-off point. Fig. 7 is a schematic flow chart of a baseline point calibration method, which specifically includes the following steps: s131) solving a local minimum value of the compressed curve to obtain a reaction equilibrium point with corresponding concentration; s132) taking the position of the reaction balance point as a reference coordinate, and acquiring an adsorption starting point according to the slope characteristic; s133) obtaining an adsorption starting point according to the reaction equilibrium point, performing reverse sequence segmented matching from the next concentration adsorption starting point to the current concentration reaction equilibrium point based on a DTW algorithm, searching a curve segment with the maximum similarity, and obtaining a desorption cut-off point.
The content of S133 is explained in detail below with reference to fig. 7.
When the gas-sensitive material and the target small molecule reach adsorption balance, the reaction balance point is located at the local minimum value of the curve, so that the reaction balance point with corresponding concentration can be obtained by solving the local minimum value of the compression frequency curve, and the reaction balance point is also the position where the gas-sensitive material has the maximum adsorption molecular weight.
On the basis, the adsorption starting point can be searched according to the position of the reaction equilibrium point as a reference coordinate. The adsorption starting point is the time point when the gas to be detected enters the reaction device, at the moment, the gas sensitive material and the gas molecules to be detected generate interface reaction, the curve can rapidly enter the adsorption stage from a stable state, the curve is rapidly reduced, and the slope of the curve is rapidly reduced. Obtaining a resonance frequency change curve after compression according to a D-P algorithm, and calculating the first derivative y of the resonance frequency change curve ds Let the characteristic point L before the equilibrium point be the starting point, let D be the product of L and the first derivative at L-1, i.e. D = y ds (L)×y ds (L-1), if D is larger than 0, making L-1 be new L, and if D is smaller than 0, indicating that the current point L is the position of the adsorption starting point.
The curve of the gas sensitive material at the end of desorption tends to be flat, and the desorption cut-off point is difficult to obtain only from the curve characteristics. Due to the stability of the resonant micro-cantilever, even if a small part of gas molecules remain, the trend change of the frequency curve of the resonant micro-cantilever is not influenced. Therefore, the curve section which tends to be gentle to the beginning of the next concentration after the material is completely desorbed is similar to the curve before adsorption, and according to the similarity of the two curve sections, the curve section with the maximum similarity degree is searched by performing reverse-order segmentation matching from the adsorption starting point of the next concentration to the reaction equilibrium point of the current concentration based on the DTW algorithm.
The DTW algorithm can effectively measure the similarity degree of two discrete sequences, and distance matching of sequences with different lengths is realized by dynamically extending or compressing the sequences on a time axis. Suppose that the two sequences are each a = [ a = [ [ a ] 1 ,a 2 …,a i ,…,a m ]And b = [ b ] 1 ,b 2 …,b j ,…,b n ]Defining a distance matrix D m×n
Element d in the matrix ij Is the Euclidean distance between two elements in the sequence, wherein
In the DTW algorithm, a path with the minimum bending cost is searched by adopting a dynamic warping method, and the dynamic warping path is defined and recorded as w = [ w ] 1 ,w 2 ,…,w l ,…,w L ]The elements in W are a set of adjacent distance matrix elements, W l ＝d ij The DTW distance is calculated from the minimum path length of the sequence A, B:
wherein the smaller the DTW distance, the greater the sequence similarity.
Referring to fig. 7, considering that the computation complexity of the DTW algorithm is O (m × n), a selective matching DTW algorithm is proposed in combination with the curve feature to reduce the number of DTW executions in the algorithm.
And (4) searching a desorption cut-off point and a curve segment with the minimum DTW distance by measuring the similarity of the curves before and after adsorption and desorption. Definition p = [ p ] 1 ,p 2 ,…,p k ]Recording dist as the minimum distance in the matching of the current concentration curve for k characteristic points to be identified from the next concentration adsorption starting point to the current concentration reaction equilibrium point, setting a threshold value of epsilon =3 × dist in combination with experience, and performing the following detailed algorithm steps:
1) Curve y of preparation stage before adsorption starting point 0 As a reference curve for distance matching;
2) The characteristic point p to be identified 1 And p 2 The curve between is taken as the first section of curve y to be matched n Calculating a reference curve y 0 With curve y to be matched n The DTW distance of (1);
3) Taking the distance as an initial value of the minimum matching distance dist, and updating the threshold epsilon;
4) The characteristic point p to be identified 1 And the next feature point p i The curve between (i is less than or equal to k) is used as a new curve y to be matched n Calculating y 0 And y n The DTW distance of (d);
5) If the matching distance is smaller than the minimum matching distance dist, updating the dist and updating the threshold epsilon, and returning to the step 4) to continuously match the next feature point; if the current distance is larger than the threshold value, namely d is larger than epsilon, the current characteristic point p i The previous feature point p of i-1 Namely the desorption cut-off point of the current concentration interval;
6) And returning to the step 1), searching a desorption cut-off point of the next concentration interval until all concentrations are searched.
By combining with the characteristic analysis of the resonant frequency curve, when the characteristic points to be identified are matched with the required desorption cut-off point, the subsequent characteristic points to be identified are all in the desorption stage of the gas sensitive material, the similarity of the subsequent curve to be matched is smaller and smaller, the curve with the smaller subsequent similarity does not need to be calculated, and the calculation cost is reduced. At this point, the three baseline points required in the baseline processing are acquired.
S2: and (4) piecewise linearly fitting a baseline according to the baseline point of each concentration, and deducting the baseline by using the initial resonance frequency change curve to obtain a standard resonance frequency change curve.
S3: and calculating the parameters of the gas-sensitive material according to the standard resonance frequency change curve.
Exemplarily, fig. 8 is a schematic diagram of a calculation flow of thermodynamic and kinetic parameters of a gas-sensitive material adsorption-desorption reaction. The parameter calculation of the gas sensitive material is a temperature-variable micro-weighing method based on micro-cantilever resonance technology, and the method converts the change of molecular adsorption quantity into the change of a resonance frequency change curve through a plurality of groups of concentration gas molecular adsorption and desorption experiments under two different temperature environments. Based on the definition of enthalpy change and Gibbs free energy change, van-Teff equation, clausius-Klebs Long Fangcheng and Langmuir equationAnd (3) waiting for the basic physical and chemical theory, calculating by one key to obtain a whole set of thermodynamic parameters, and establishing a quantitative model reflecting the physical and chemical essence of the material. Under the same partial pressure of different temperatures, the adsorption rate constant can be obtained from the slope of the resonance frequency change curve, and then the kinetic quantitative evaluation parameter activation energy is obtained according to the arrhenius equation. The thermodynamic and kinetic parameters obtained by the automatic parameter extraction of the temperature-changing micro-weighing method are numerous, wherein the thermodynamic parameters comprise enthalpy change (delta H), entropy change (delta S) and Gibbs free energy change (delta G); kinetic parameters include the adsorption/desorption rate constant (K) a /K d ) Total number of active sites (N), coverage (θ), equilibrium constant (K), activation energy (E) a ). If the absorption and desorption type and efficiency of the material to be detected are judged by only one parameter, misjudgment with a larger probability can be generated, so that a scientific and normative gas sensitive material evaluation method is established and comprehensive and objective evaluation is carried out, and the method has important significance on the efficiency of gas sensitive material research.
S4: and selecting parameters of the gas sensitive material to construct a gas sensitive material evaluation decision model, and performing multi-attribute decision on the gas sensitive material.
Illustratively, fig. 9 is an evaluation decision model of an optimal solution of the gas sensitive material. As the thermodynamic and kinetic parameters are numerous, the expert opinion is combined, five attributes with typical characteristics are selected as evaluation criteria, and the enthalpy change (delta H) in the thermodynamic parameters is selected to judge the adsorption and desorption type and the adsorption/desorption rate constant (K) in the kinetic parameters from three aspects of reaction type, reaction speed and adsorption capacity respectively a /K d ) To evaluate the reaction rate, the total number of active sites (N) and the coverage (θ) were used to evaluate the adsorption capacity.
Aiming at a target problem, an evaluation decision model is decomposed into three layers, namely a target layer, a criterion layer and a scheme layer, wherein the criterion layer comprises five evaluation attributes and respectively covers typical thermodynamics and kinetics attributes of the gas-sensitive material, and the scheme layer is a list of a decision material set to be selected.
Fig. 10 is a flowchart illustrating a method for performing multi-attribute decision-making on a gas sensitive material by the VIKOR method. The VIKOR (multi-criterion compromise solution ranking) method is a multi-attribute decision-making method based on ideal points and compromise ranking, and is characterized in that the maximum group effectiveness and the minimum individual regressions can be considered at the same time, and the gas sensitive material selection scheme is subjected to compromise ranking by the idea closest to the ideal solution.
The basic idea of the VIKOR is to first determine a positive ideal solution and a negative ideal solution, where the positive ideal solution refers to the best value of each alternative in each evaluation criterion and the negative ideal solution refers to the worst value of each solution in each evaluation criterion. The schemes are then prioritized according to how close the respective evaluated values of the respective candidate decision materials are to the ideal scheme. In the comprehensive evaluation, VIKOR used the following L p -metric aggregation function:
in the formula, p is more than or equal to 1 and less than or equal to infinity, J =1,2, …, J, w i Representing the weight of the standard attribute, the variable J representing the number of decision-making materials to be selected, and each decision-making material to be selected uses a j Is shown as f ij Representing candidate decision material a j Measure L of the property value of the ith criterion p,j Indicating Material a j Distance to ideal solution, f i * Positive ideal solution, f, representing the ith criterion attribute i - Representing a negative ideal solution for the ith criterion attribute. Assume that all alternative decision schemes have m, denoted as A = [ A = 1 ,A 2 ,…,A m ] T N attribute indexes of the decision are marked as C = [) 1 ,C 2 ,…,C n ] T Recording scheme A i Has an attribute value of a ij (1. Ltoreq. I.ltoreq.m, 1. Ltoreq. J.ltoreq.n), matrix A = (a) ij ) m×n Is a decision matrix.
Referring to FIG. 10, the multi-attribute decision making for the gas sensitive material using the VIKOR method includes the following steps:
s41: and performing standardization processing on the attribute values of the decision-making materials to be selected, determining the weight of each attribute based on an improved entropy weight method, and determining a positive ideal solution and a negative ideal solution of each attribute. The method specifically comprises the following steps:
(a) And acquiring decision-making materials to be selected, and considering that different attributes of the decision-making materials to be selected often have different dimensions and dimension units, standardizing the attribute values of the decision-making materials to be selected. For example, by using Min-max standardization (Min-max Normalization), the attributes of the scheme are classified into benefit type attributes, cost type attributes and moderate type attributes, and specifically, the method includes:
and (3) standardizing benefit type attributes:
wherein the higher the decision attribute value, the better;
cost-type attribute standardization:
wherein the lower the decision attribute value, the better;
medium-sized attribute normalization:
with decision attribute values of moderate size.
A in the above formula j Represents each candidate decision material, a ij Representing candidate decision material A i The attribute value of the jth evaluation criterion of (1).
(b) And determining the weight of each attribute based on the improved entropy weight method. The VIKOR method requires that the weight of each attribute is known at the time of decision making, and the weight vector is w = [ w = 1 ,w 2 ,…,w n ] T Determining each criterion weight by adopting an improved entropy weight method, and offsetting decision matrix data, wherein the specific calculation steps are as follows:
b1 Determine an attribute entropy value P j ：
Wherein K =1/ln (m), j =1,2, …, n, f ij Representing candidate decision material a j The attribute value of the ith criterion, r ij Representing an offset to the original attribute value.
b2 ) determine the attribute difference coefficient H j ：
H j ＝1-P j
In the formula, P j Representing an attribute entropy value.
b3 Determine attribute weights: the attribute difference coefficient is subjected to standardization processing to obtain a weight, and the calculation formula is as follows:
in the formula, H j Representing the attribute difference coefficient.
(c) Determining a positive ideal solution f for each attribute i * And negative ideal solution f i - ：
In the formula I 1 Is a benefit type index set; i is 2 For cost-based index set, f ij Representing candidate decision material a j The attribute value of the ith criterion.
S42: according to the positive ideal solution f i * And negative ideal solution f i - Calculating the group utility value S of the comprehensive evaluation of each candidate decision material i And individual regret value R i ：
R j ＝m i ax[w i (f i * -f ij )/(f i * -f i - )]
In the formula: w is a i Weights, S, representing respective attributes j And R j Indicating an evaluation value of a negative property.
S43: calculating the profit ratio value Q generated by each decision-making material to be selected according to the group utility value and the individual regret value j ，Q j ＝v(S j -S * )/(S - -S * )+(1-v)(R j -R * )/(R - -R * ) In the formula:
v represents the weight or maximum group utility value of the 'majority criterion' strategy, wherein v =0.5 can be taken as the group utility maximization and the individual regret minimization, Q j Is the comprehensive evaluation value of the jth scheme and is based on the group utility value S i Individual regret value R i And a value of interest ratio Q i And determining the arrangement sequence of the decision-making materials to be selected. />
S44: and determining a compromise scheme according to the arrangement sequence of the decision-making materials to be selected. Compromise of scheme set a (1) Comprises the following steps: a is (1) Is the first-ranked scheme of Q and satisfies the following condition:
condition 1: q (a) (2) -Q(a (1) ) Is not less than 1/(m-1), wherein a is (1) For the best solution in the ordered list by Q, a (2) And m is the number of schemes.
Condition 2: a is (1) Is the previous scheme of S or R. I.e., the previous scheme ranked by Q, the population utility value S or the individual regret value R is also smaller.
If one of the above conditions is not met:
(a) If Condition 2 is not satisfied, scenario a (1) And a (2) Are all trade-off solutions.
(b) If Condition 1 is not fullFoot, scheme a (1) ,a (2) ,…a (r) Is a compromise thereof, wherein a (r) Satisfies the condition Q (a) (r) -Q(a (1) ))≥1/(m-1)。
In summary, the method is based on the micro-cantilever resonance technology, the method for establishing the automatic calibration of the resonance frequency output signal baseline point, the baseline correction and the multi-attribute decision by combining the temperature-changing micro-weighing method is combined, the problem of baseline drift of the output signal is solved, meanwhile, the automatic calculation of the gas-sensitive material parameters is realized, the experimental efficiency of the gas-sensitive material can be greatly improved, accidental errors are reduced, the multi-attribute decision of the gas-sensitive material is realized by constructing a gas-sensitive material evaluation decision model, and the optimal gas-sensitive material can be selected according to different application occasions.
Example two
In the following, a signal processing and multi-attribute decision making system based on micro-cantilever gas sensitive material analysis according to a second embodiment of the present invention is introduced, and a signal processing and multi-attribute decision making system based on micro-cantilever gas sensitive material analysis described below and a signal processing and multi-attribute decision making method based on micro-cantilever gas sensitive material analysis described above may be referred to in a corresponding manner.
Referring to fig. 11, a signal processing and multi-attribute decision making system based on micro-cantilever gas sensitive material analysis according to a second embodiment of the present invention includes:
the baseline point automatic calibration module 100 is used for compressing the resonance frequency change curve, and the baseline point automatic calibration module 100 is used for automatically calibrating baseline points of various concentrations on the compressed curve;
the baseline correction module 200 is used for piecewise linearly fitting a baseline according to the baseline point of each concentration, and deducting the baseline by using the initial resonant frequency change curve to obtain a standard resonant frequency change curve;
the parameter calculation module 300, the parameter calculation module 300 is used for calculating the characteristic parameters of the gas-sensitive material according to the standard resonant frequency variation curve;
the multi-attribute decision module 400 and the multi-attribute decision module 40 are used for selecting characteristic parameters of typical gas-sensitive materials to construct a gas-sensitive material evaluation decision model and perform multi-attribute decision on the gas-sensitive materials.
The signal processing and multi-attribute decision system based on micro-cantilever gas-sensitive material analysis of the present embodiment is used for implementing the signal processing and multi-attribute decision method based on micro-cantilever gas-sensitive material analysis, and therefore, the specific implementation of the system can be found in the foregoing embodiments of the signal processing and multi-attribute decision method based on micro-cantilever gas-sensitive material analysis, and therefore, the specific implementation thereof can refer to the description of the corresponding embodiments of each part, and will not be further described herein.
In addition, since the signal processing and multi-attribute decision system based on the micro-cantilever gas-sensitive material analysis of the embodiment is used for implementing the signal processing and multi-attribute decision method based on the micro-cantilever gas-sensitive material analysis, the function of the system corresponds to that of the method, and the description is omitted here.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (7)
1. A signal processing and multi-attribute decision method based on micro-cantilever gas sensitive material analysis is characterized by comprising the following steps:
s1: compressing the resonance frequency change curve, and automatically calibrating the baseline point of each concentration on the compressed curve;
s2: fitting a baseline according to the baseline points of each concentration in a piecewise linear manner, and deducting the baseline by using the initial resonance frequency change curve to obtain a standard resonance frequency change curve;
s3: calculating characteristic parameters of the gas-sensitive material according to the standard resonance frequency change curve;
s4: selecting characteristic parameters of a typical gas sensitive material to construct a gas sensitive material evaluation decision model, and performing multi-attribute decision on the gas sensitive material;
the step S1 includes the steps of:
s11: obtaining a resonance frequency change curve, and smoothing the resonance frequency change curve;
s12: compressing the curve after the smoothing treatment by adopting a D-P algorithm;
s13: calibrating the baseline point of each concentration on the compressed curve;
in step S12, compressing the smoothed curve by using a D-P algorithm includes:
s121: connecting two end points A, B of the curve into a straight line AB;
s122: calculating the distance from each point on the curve to the straight line AB to obtain a point C with the maximum distance;
s123: comparing the distance from the point C to the straight line AB with a preset threshold value of a D-P algorithm, and when the distance from the point C to the straight line AB is less than or equal to the threshold value, representing the section of curve by using the straight line AB;
s124: when the distance from the point C to the straight line AB is greater than the threshold value, the point C is used as a separation point to divide the curve, and the processing from S121 to S123 is repeated on the curve segments AC and BC;
s125: after all the curve sections are processed, the broken line sections formed by connecting all the separation points approximately represent the curve.
2. The micro-cantilever gas sensitive material analysis-based signal processing and multi-attribute decision method of claim 1, wherein: calibrating the baseline points for each concentration on the compressed curve includes:
s131: solving a local minimum value of the compressed curve to obtain a reaction equilibrium point with corresponding concentration;
s132: taking the position of the reaction equilibrium point as a reference coordinate, and acquiring an adsorption starting point according to the slope characteristics;
s133: based on the DTW algorithm, the next concentration adsorption initial point is matched with the current concentration reaction equilibrium point in a reverse sequence and subsection mode, the curve section with the maximum similarity is searched, and the desorption cut-off point is obtained.
3. The micro-cantilever gas sensitive material analysis-based signal processing and multi-attribute decision method as claimed in claim 1, wherein: the characteristic parameters of the gas sensitive material comprise thermodynamic parameters and kinetic parameters, wherein the thermodynamic parameters comprise enthalpy change, entropy change and Gibbs free energy change, and the kinetic parameters comprise absorption/desorption rate constant, total active site number, coverage, equilibrium constant and activation energy.
4. The micro-cantilever gas sensitive material analysis-based signal processing and multi-attribute decision method as claimed in claim 1, wherein: the gas sensitive material evaluation decision model at least comprises a target layer, a criterion layer and a scheme layer, wherein the criterion layer comprises a plurality of evaluation attributes, and the scheme layer comprises a candidate decision material set.
5. The micro-cantilever gas sensitive material analysis-based signal processing and multi-attribute decision method as claimed in claim 1, wherein: making multi-attribute decisions on gas sensitive materials includes:
performing multi-attribute decision on the gas sensitive material by using a VIKOR method, wherein the VIKOR adopts the following L in the decision p -metric aggregation function:
in the formula, p is more than or equal to 1 and less than or equal to infinity, J =1,2,L,J, and variable J represents the number of decision-making materials to be selected, and each decision-making material to be selected uses a j Is shown as f ij Representing candidate decision material a j Measure L of the property value of the ith criterion p,j Representation scheme a j Distance to ideal solution, f i * Positive ideal solution, f, representing the ith criterion attribute i - Negative ideal solution, w, representing the ith criterion attribute i Representing the weight of the ith criterion attribute.
6. The micro-cantilever gas sensitive material analysis-based signal processing and multi-attribute decision method as claimed in claim 5, wherein: the method for performing multi-attribute decision on the gas sensitive material by using the VIKOR method comprises the following steps:
standardizing the attribute values of the decision-making materials to be selected, determining the weight of each attribute based on an improved entropy weight method, and determining a positive ideal solution and a negative ideal solution of each attribute;
calculating the group utility value and the individual regret value of the comprehensive evaluation of each decision material to be selected according to the positive ideal solution and the negative ideal solution;
calculating benefit ratio values generated by the decision-making materials to be selected according to the group utility values and the individual regret values, and determining the arrangement sequence of the decision-making materials to be selected according to the group utility values, the individual regret values and the benefit ratio values;
and determining a compromise scheme according to the arrangement sequence of the decision-making materials to be selected.
7. A signal processing and multi-attribute decision making system based on micro-cantilever gas sensitive material analysis is characterized by comprising:
the base line point automatic calibration module is used for compressing the resonance frequency change curve, and the base line point of the base line point automatic calibration module is used for automatically calibrating the base line point of each concentration on the compressed curve;
the baseline correction module is used for piecewise linearly fitting a baseline according to the baseline point of each concentration and deducting the baseline by using an initial resonance frequency change curve to obtain a standard resonance frequency change curve;
the parameter calculation module is used for calculating characteristic parameters of the gas-sensitive material according to the standard resonance frequency change curve;
the multi-attribute decision module is used for selecting characteristic parameters of a typical gas sensitive material to construct a gas sensitive material evaluation decision model and performing multi-attribute decision on the gas sensitive material;
the baseline point automatic calibration module executes the following steps:
obtaining a resonant frequency change curve, and smoothing the resonant frequency change curve;
compressing the curve after the smoothing treatment by adopting a D-P algorithm;
calibrating the baseline point of each concentration on the compressed curve;
the method for compressing the curve subjected to the smoothing processing by adopting the D-P algorithm comprises the following steps:
connecting two end points A, B of the curve into a straight line AB;
calculating the distance from each point on the curve to the straight line AB to obtain a point C with the maximum distance;
comparing the distance from the point C to the straight line AB with a preset threshold value of a D-P algorithm, and when the distance from the point C to the straight line AB is less than or equal to the threshold value, representing the section of curve by using the straight line AB;
when the distance from the point C to the straight line AB is greater than the threshold value, the point C is used as a separation point to divide the curve, and the processing from S121 to S123 is repeated on the curve segments AC and BC;
after the processing of all the curve segments is finished, the broken line segments connecting all the separation points approximately represent the curve.
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