CN116400028A - Essence quality detection method, system and medium based on smell sensor - Google Patents
Essence quality detection method, system and medium based on smell sensor Download PDFInfo
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- 125000004435 hydrogen atom Chemical class [H]* 0.000 claims description 6
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Abstract
The invention discloses an essence quality detection method system based on an odor sensor and a medium, wherein the method comprises the steps of setting an odor sensor array, preprocessing data based on a one-dimensional convolutional neural network, splicing and fusing a class probability vector and a feature vector after dimension reduction processing, and identifying according to fused data features based on an attraction search algorithm to obtain a matched essence quality grade. According to the invention, the dimension reduction and fusion of the features are carried out by combining the correlation degree and the redundancy degree of the data features, so that important feature information including global information is reserved, the redundancy features are removed, and the calculation difficulty is reduced; and the gravity search algorithm is based on the recognition of the fused data characteristics, so that the automatic recognition and matching of the essence quality are realized, and the essence quality can be detected rapidly and accurately.
Description
Technical Field
The invention relates to the field of essence quality detection, in particular to an essence quality detection method system based on an odor sensor and a medium.
Background
The essence and spice in the food is an important production raw material for food production, the formula of the spice and spice naturally becomes the core technology of a production enterprise, and the quality of the essence and spice is closely related to the brand. With the continuous progress of the production process, people have higher and higher requirements on the quality and flavor of products, and essence and spice are widely applied to the food production industry as an article which can influence the mouthfeel and flavor of foods. In recent years, the health awareness of people is gradually enhanced, so that manufacturers need to exert force to improve the safety of foods, the use of essence and spice is more and more careful, once the content of the essence and spice in the product is reduced, the food is inevitably caused, the mouthfeel of the food is influenced, the original style of the product is lost, and the product is difficult to be accepted by wide consumers. Therefore, the flavor and fragrance content of the product is reduced, and the formula of the flavor and fragrance is optimized to ensure the taste and fragrance of the product.
The existing detection methods for the essence and the spice are gas chromatography, infrared absorption spectrum analysis method, ultraviolet absorption spectrum analysis method and the like, the preparation and experimental processes of the method are complex, the required time is long, a large number of samples cannot be detected, and the quick and accurate detection of the essence and the spice samples cannot be realized.
Disclosure of Invention
In order to solve the technical problems, the invention provides an essence quality detection method system based on an odor sensor and a medium. The method is based on the smell sensors, and realizes automatic identification and matching of essence quality by data acquisition and fusion of a plurality of smell sensors, so that quick and accurate essence quality detection is realized.
The invention is realized by the following technical scheme:
an essence quality detection method based on an odor sensor comprises the following steps:
s1: providing an odor sensor array, wherein the odor sensor array comprises a W1C sensor for sensing aromatic compounds, a W5S sensor for sensing nitrogen oxides, a W3C sensor for sensing ammonia and aromatic compounds, a W6S sensor for sensing hydrogen, a W1W sensor for sensing sulfides, a W2S sensor for sensing ethanol and a W3S sensor for sensing alkanes;
s2: acquiring odor sensor array data;
s3: carrying out data preprocessing on the data based on a one-dimensional convolutional neural network;
s4: fusing the data of the multiple sensors, wherein the data features of the multiple sensors extracted in the step S3 are fused;
the fusion method comprises the steps of splicing and fusing the category probability vector and the feature vector subjected to the dimension reduction treatment to obtain fused features;
the class probability vector is obtained by utilizing original input features to respectively perform feature training based on different classifiers;
s5: and identifying according to the fused data characteristics based on an gravitation search algorithm to obtain the matched essence quality grade.
Further, the one-dimensional convolutional neural network in the step S3 includes 4 convolutional layers, 3 pooling layers and one flattening layer, and the convolutional kernel size of the convolutional layers is 3.
Further, the category probability vector in the step S4 is obtained by the following method:
and performing feature training by utilizing original input features based on different classifiers respectively, wherein the classifiers comprise random forests, XGBoost and GBDT, and obtaining a class probability vector through training.
Further, the dimension reduction processing in step S4 includes calculating a correlation coefficient and redundancy, and performing dimension reduction processing according to the correlation coefficient and redundancy, where the dimension reduction processing includes deleting a feature with a correlation lower than a set threshold and randomly selecting one of features with a redundancy higher than the set threshold, so as to obtain a feature after the dimension reduction processing.
Further, the correlation coefficient is calculated as follows:
of the formula (I)
and />
The average value of the quality of the feature vector and the essence is +.>
The number of samples is represented here as the sample size.
Further, the redundancy calculation method is as follows:
for a matrix D formed by one feature A and another feature B extracted by a sensor, firstly, distributing data points in the D in a two-dimensional plane by using a maximum information coefficient method, and carrying out grid division on the plane by using aXb straight lines, and calculating to obtain probability distribution of the data points falling into a demarcation grid, namely mutual information equivalent to the information between A and B under the division condition, wherein the probability distribution is shown as the following formula:
wherein ,
、/>
、/>
the joint distribution and the edge distribution of the two features respectively.
The maximum information coefficient between the features is further calculated, and the process is as follows:
where R is a function of the sample size,
， />
for the number of test samples, +.>
To adjust the parameters.
Further, the identifying based on the gravitation search algorithm according to the fused data features comprises:
s51: algorithm parameter initialization including setting population size to N, solution range [ up, down ]]Dimension dim of solution, maximum iteration number T, initial gravitational constant G 0 The method comprises the steps of carrying out a first treatment on the surface of the At [ up, down ]]Randomly generating an initial population in a range;
s52: calculating an fitness value according to the fitness function;
s53: find the optimal fitness value F best And corresponding location information L thereof best ；
S54: updating the individual quality matrix M according to the following formula, and obtaining a population quality matrix M through normalization;
wherein ,
for the fitness of particle i +.>
Is the optimal value at the t-th iteration, < >>
Is the worst value at the t-th iteration; />
The particle quality after normalization;
s55: updating the gravitational constant G according to the following formula;
wherein ,
is the initial gravitational constant, +.>
Is constant, T is the maximum number of iterations;
s56: calculating Euclidean distance between particles i and j, and calculating attraction force F between particles i and j on the kth dimension at the t-th iteration according to the following ij ；
wherein ,
is gravitational constant, ++>
Is the Euclidean distance between particle i and particle j;
s57: updating the resultant force F and the particle acceleration a suffered by the particle i according to the following formula;
the total force to which the particle i is subjected in the k-th dimension is equal to the random weighted sum of the forces of the other particles:
wherein p is a random parameter representing a random weight.
The acceleration of the particles is calculated according to the following formula:
s58: updating particle velocity and position according to
wherein ,
is the k-dimensional component of the position of particle i, < >>
Is the k-dimensional component of the velocity of particle i, < >>
、/>
、/>
Is [0,1]Random parameters in->
、/>
For learning factors->
For the current optimal position of the kth dimension individual, < >>
The k-th dimension global optimal position;
by adjusting
、/>
The value of (2) can adjust the degree of influence of individuals and groups in the particle movement process;
s59: calculating a mutation trigger function value, and judging whether the mutation trigger function value meets a mutation condition or not;
as the number of iterations increases, the amplitude of the movement of the particles decreases significantly. Therefore, the algorithm is improved based on the self-adaptive mutation method, meanwhile, the mutation triggering rate is reduced along with the increase of the iteration times, the diversity of the population is improved by using a higher mutation rate at the initial stage of the algorithm, the global optimizing capability of the algorithm is enhanced, the mutation rate is reduced at the later stage, the damage to good individuals is prevented, and a foundation is provided for obtaining higher precision. The mutation trigger function value calculation method comprises the following steps:
wherein ,
for the abrupt trigger value of the ith particle in the t-th iteration, when +.>
When the threshold value is larger than the set threshold value, triggering abrupt change; p is a random parameter, dim is the dimension of the solution,>
is a smaller constant;
s510: performing position uniform mutation
The uniform mutation update particle location formula is as follows:
wherein p is a random parameter ranging between [0,1 ];
s511: and (5) carrying out loop iteration, and outputting a global optimal solution, namely the quality grade of the matched essence.
Further, in the step S58
、/>
Calculated according to the following formula:
wherein G is an gravitation constant, T is the current iteration number, and T is the maximum iteration number.
The invention also provides an essence quality detection system based on the odor sensor, which comprises:
an odor sensor array comprising a W1C sensor for sensing aromatic compounds, a W5S sensor for sensing nitrogen oxides, a W3C sensor for sensing ammonia and aromatic compounds, a W6S sensor for sensing hydrogen, a W1W sensor for sensing sulfides, a W2S sensor for sensing ethanol, a W3S sensor for sensing alkanes;
the data preprocessing module is used for preprocessing sensor data based on the one-dimensional convolutional neural network;
the multi-sensor data fusion module is used for fusing the data characteristics of the plurality of sensors, and the fusion method is to splice and fuse the category probability vector and the feature vector subjected to the dimension reduction treatment to obtain the fused features;
the class probability vector is obtained by utilizing original input features to respectively perform feature training based on different classifiers;
and the feature recognition module is used for recognizing the fused data features based on an gravitation search algorithm.
In addition, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon program instructions of an aroma-sensor-based aroma quality detection method, the program instructions of the aroma-sensor-based aroma quality detection being executable by one or more processors to implement the steps of the aroma-sensor-based aroma quality detection method as described above.
Compared with the prior art, the invention has the beneficial effects that: the feature dimension reduction and fusion are carried out by combining the relativity and redundancy of the data features, so that important feature information including global information is reserved, the redundancy features are removed, and the calculation difficulty is reduced; and the gravity search algorithm is based on the recognition of the fused data characteristics, so that the automatic recognition and matching of the essence quality are realized, and the essence quality can be detected rapidly and accurately.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic flow chart of a perfume quality detection method based on a perfume smell sensor according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a multi-sensor data fusion method according to an embodiment of the present application;
fig. 3 is a schematic flow diagram of a feature recognition algorithm based on an gravity search algorithm according to an embodiment of the present application.
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Referring to fig. 1, a fragrance quality detection method based on a fragrance sensor includes the following steps:
s1: providing an array of odour sensors
The type of odor sensor is selected according to the components of the essence and the components of impurities in the common essence, and comprises a W1C sensor for sensing aromatic compounds, a W5S sensor for sensing nitrogen oxides, a W3C sensor for sensing ammonia and aromatic compounds, a W6S sensor for sensing hydrogen, a W1W sensor for sensing sulfides, a W2S sensor for sensing ethanol and a W3S sensor for sensing alkanes.
S2: acquisition of smell sensor array data
Sensor data is acquired from the sensor array in step S1.
S3: data preprocessing
Because the sensitive substances of the sensors have certain overlapping, the n sensors have stronger cross sensitivity, and the data needs to be processed in order to improve the usability of the data. The invention processes data based on a one-dimensional convolutional neural network, and the specific method is as follows:
the data comprises response values of n sensors, and if the data volume of each sensor is m, the sensor data is
The matrix of (2) converts the data into n channels of one-dimensional convolutional neural network, and the network outputs k eigenvectors.
Optionally, the one-dimensional convolutional neural network comprises 4 convolutional layers, 3 pooling layers and a flattening layer, the convolutional kernel size of the convolutional layers is 3, and ReLU is selected as an activation function; the step length of the first two pooling layers is 2, the size of the filter is 2, the step length of the last pooling layer is 1, and the size of the filter is 3; and finally, converting the two-dimensional feature matrix into k feature vectors through a flattening layer. Optionally, k is 128.
S4: and (3) fusing the data characteristics of the plurality of sensors extracted in the step (S3), wherein the data fusion method is shown in fig. 2:
s41: the method comprises the steps of performing feature training by utilizing original input features based on different classifiers respectively, wherein the classifiers comprise random forest, XGBoost and GBDT, and obtaining a class probability vector P through training 1 ，P 2 ，…P j ;
S42, performing dimension reduction processing on the original input features to obtain feature vectors T after the dimension reduction processing;
the dimension reduction processing comprises the steps of calculating a correlation coefficient and redundancy, and performing dimension reduction processing according to the correlation coefficient and redundancy, and comprises the following steps:
s421: the correlation coefficient between each characteristic and the essence quality is calculated, and the calculation method is as follows:
of the formula (I)
and />
The average value of the quality of the feature vector and the essence is +.>
The number of samples is represented here as the sample size.
S422: calculating redundancy between features
The lower the correlation between the two variables, the less overlapping their information, i.e., the lower the redundancy. The invention calculates redundancy between every two features based on a maximum information coefficient method, and comprises the following steps:
for a matrix D formed by one feature A and another feature B extracted by a sensor, firstly, distributing data points in the D in a two-dimensional plane by using a maximum information coefficient method, and carrying out grid division on the plane by using aXb straight lines, and calculating to obtain probability distribution of the data points falling into a demarcation grid, namely mutual information equivalent to the information between A and B under the division condition, wherein the probability distribution is shown as the following formula:
wherein ,
、/>
、/>
the joint distribution and the edge distribution of the two features respectively.
Further calculating a maximum information coefficient between features, the process of which is shown in the following formula, wherein R is a sample capacity function, calculating the maximum information coefficient between features,
， />
for the number of test samples, +.>
The value of (2) affects the universality of the maximum information coefficient method, and optionally, the value is 0.6.
S423: dimension reduction treatment
Deleting the features with the correlation lower than the set threshold value and randomly selecting one of the features with the redundancy higher than the set threshold value, thereby obtaining the features after the dimension reduction processing.
S43: and splicing and fusing the category probability vector and the feature vector subjected to the dimension reduction treatment to obtain fused features.
Based on the method, important characteristic information including global information is reserved, redundant characteristics are removed, and calculation difficulty is reduced.
S5: based on the gravitation search algorithm, the recognition is carried out according to the fused data characteristics, and the quality grade of the matched essence is obtained, as shown in figure 3, and the method comprises the following steps:
s51: algorithm parameter initialization including setting population size to N, solution range [ up, down ]]Dimension dim of solution, maximum iteration number T, initial gravitational constant G 0 The method comprises the steps of carrying out a first treatment on the surface of the At [ up, down ]]Randomly generating an initial population in a range;
s52: calculating an fitness value according to the fitness function;
optionally, the fitness function is a 5-fold cross-validation accuracy.
S53: find the optimal fitness value F best And corresponding location information L thereof best ；
S54: updating the individual quality matrix M according to the following formula, and obtaining a population quality matrix M through normalization;
wherein ,
for the fitness of particle i +.>
Is the optimal value at the t-th iteration, < >>
Is the worst value at the t-th iteration; />
Is the normalized particle mass.
S55: updating the gravitational constant G according to the following formula;
wherein ,
is the initial gravitational constant, +.>
Is constant, T is the maximum number of iterations.
S56: calculating Euclidean distance between particles i and j, and calculating attraction force F between particles i and j on the kth dimension at the t-th iteration according to the following ij ；
wherein ,
is gravitational constant, ++>
Is the euclidean distance between particle i and particle j.
S57: updating the resultant force F and the particle acceleration a suffered by the particle i according to the following formula;
the total force experienced by particle i in the k-th dimension is equal to the random weighted sum of the forces of the other particles.
Wherein p is a random parameter representing a random weight.
The acceleration of the particles is calculated according to the following formula:
s58: updating particle velocity and position according to
wherein ,
is the k-dimensional component of the position of particle i, < >>
Is the k-dimensional component of the velocity of particle i, < >>
、/>
、/>
Is [0,1]Random parameters in->
、/>
For learning factors->
For the current optimal position of the kth dimension individual, < >>
Is the k-th dimension global optimum position.
By adjusting
、/>
The value of (2) can be used to adjust the degree of influence of the individual and population during the movement of the particles, optionally,/->
、/>
Calculated according to the following formula:
wherein G is an gravitation constant, T is the current iteration number, and T is the maximum iteration number.
S59: calculating a mutation trigger function value, and judging whether the mutation trigger function value meets a mutation condition or not;
as the number of iterations increases, the amplitude of the movement of the particles decreases significantly. Therefore, the algorithm is improved based on the self-adaptive mutation method, meanwhile, the mutation triggering rate is reduced along with the increase of the iteration times, the diversity of the population is improved by using a higher mutation rate at the initial stage of the algorithm, the global optimizing capability of the algorithm is enhanced, the mutation rate is reduced at the later stage, the damage to good individuals is prevented, and a foundation is provided for obtaining higher precision. The mutation trigger function value calculation method comprises the following steps:
wherein ,
for the abrupt trigger value of the ith particle in the t-th iteration, when +.>
When the threshold value is larger than the set threshold value, triggering abrupt change; p is a random parameter, dim is the dimension of the solution,>
is a smaller constant, optionally +.>
。
S510: performing position uniform mutation
For an individual meeting the mutation condition, an algorithm is improved by adopting a uniform mutation method, particles meeting the condition can continue to move with a larger amplitude based on a certain amplitude, so that the particles are close to an optimal solution area, and particles not meeting the condition can keep the original amplitude movement. The moving mode can enable particles to move to the vicinity of the optimal solution area rapidly, so that the convergence speed of an algorithm is improved. The uniform mutation update particle location formula is as follows:
wherein p is a random parameter ranging between [0,1 ].
S511: and (5) carrying out loop iteration, and outputting a global optimal solution, namely the quality grade of the matched essence.
In the embodiment, the dimension reduction and fusion of the features are performed by combining the correlation degree and the redundancy degree of the data features, so that important feature information including global information is reserved, the redundancy features are removed, and the calculation difficulty is reduced; and the gravity search algorithm is based on the recognition of the fused data characteristics, so that the automatic recognition and matching of the essence quality are realized, and the essence quality can be detected rapidly and accurately.
The embodiment of the invention also provides an essence quality detection system based on the odor sensor, which comprises the following steps:
an odor sensor array comprising a W1C sensor for sensing aromatic compounds, a W5S sensor for sensing nitrogen oxides, a W3C sensor for sensing ammonia and aromatic compounds, a W6S sensor for sensing hydrogen, a W1W sensor for sensing sulfides, a W2S sensor for sensing ethanol, a W3S sensor for sensing alkanes;
the data preprocessing module is used for preprocessing sensor data based on the one-dimensional convolutional neural network;
the multi-sensor data fusion module is used for fusing the data characteristics of a plurality of sensors, and the data fusion method comprises the following steps:
the method comprises the steps of performing feature training by utilizing original input features based on different classifiers respectively, wherein the classifiers comprise random forest, XGBoost and GBDT, and obtaining a class probability vector P through training 1 ，P 2 ，…P j ;
Performing dimension reduction processing on the original input features to obtain feature vectors T after the dimension reduction processing;
and splicing and fusing the category probability vector and the feature vector subjected to the dimension reduction treatment to obtain fused features.
And the feature recognition module is used for recognizing the fused data features based on an gravitation search algorithm.
In addition, an embodiment of the present invention further proposes a computer readable storage medium, on which program instructions of an aroma-sensor-based aroma quality detection method are stored, where the program instructions of the aroma-sensor-based aroma quality detection program can be executed by one or more processors to implement the steps of the aroma-sensor-based aroma quality detection method as described above.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (10)
1. The essence quality detection method based on the smell sensor is characterized by comprising the following steps of:
s1: providing an odor sensor array, wherein the odor sensor array comprises a W1C sensor for sensing aromatic compounds, a W5S sensor for sensing nitrogen oxides, a W3C sensor for sensing ammonia and aromatic compounds, a W6S sensor for sensing hydrogen, a W1W sensor for sensing sulfides, a W2S sensor for sensing ethanol and a W3S sensor for sensing alkanes;
s2: acquiring odor sensor array data;
s3: carrying out data preprocessing on the data based on a one-dimensional convolutional neural network;
s4: fusing the data of the multiple sensors, wherein the data features of the multiple sensors extracted in the step S3 are fused;
the fusion method comprises the steps of splicing and fusing the category probability vector and the feature vector subjected to the dimension reduction treatment to obtain fused features;
the class probability vector is obtained by utilizing original input features to respectively perform feature training based on different classifiers;
s5: and identifying according to the fused data characteristics based on an gravitation search algorithm to obtain the matched essence quality grade.
2. The fragrance quality testing method based on the fragrance sensor according to claim 1, wherein the one-dimensional convolutional neural network in the step S3 comprises 4 convolutional layers, 3 pooling layers and one flattening layer, and the convolutional layer has a convolutional kernel size of 3.
3. The flavor quality detection method based on the odor sensor according to claim 2, wherein the category probability vector in the step S4 is obtained by the following method:
and performing feature training by utilizing original input features based on different classifiers respectively, wherein the classifiers comprise random forests, XGBoost and GBDT, and obtaining a class probability vector through training.
4. The method for detecting the quality of essence based on an odor sensor according to claim 3, wherein the dimension reduction processing in the step S4 includes calculating a correlation coefficient and redundancy, and performing dimension reduction processing according to the correlation coefficient and redundancy, wherein the dimension reduction processing includes deleting a feature having a correlation lower than a set threshold and randomly selecting one of features having a redundancy higher than the set threshold, thereby obtaining a feature after the dimension reduction processing.
5. The flavor quality detection method based on the odor sensor according to claim 4, wherein the correlation coefficient is calculated as follows:
of the formula (I)
and />
The average value of the quality of the feature vector and the essence is +.>
The number of samples is represented here as the sample size.
6. The flavor quality detection method based on the odor sensor according to claim 5, wherein the redundancy calculation method is as follows:
for a matrix D formed by one feature A and another feature B extracted by a sensor, firstly, distributing data points in the D in a two-dimensional plane by using a maximum information coefficient method, and carrying out grid division on the plane by using aXb straight lines, and calculating to obtain probability distribution of the data points falling into a demarcation grid, namely mutual information equivalent to the information between A and B under the division condition, wherein the probability distribution is shown as the following formula:
wherein ,
、/>
、/>
the two features are respectively combined distribution and edge distribution;
the maximum information coefficient between the features is calculated, and the process is as follows:
where R is a function of the sample size,
， />
for the number of test samples, +.>
To adjust the parameters.
7. The scent sensor-based essence quality detection method of claim 1, wherein the gravity-based search algorithm identifies based on the fused data features comprises:
s51: algorithm parameter initialization including setting population size to N, solution range [ up, down ]]Dimension dim of solution, maximum iteration number T, initial gravitational constant G 0 The method comprises the steps of carrying out a first treatment on the surface of the At [ up, down ]]Randomly generating an initial population in a range;
s52: calculating an fitness value according to the fitness function;
s53: find the optimal fitness value F best And corresponding location information L thereof best ；
S54: updating the individual quality matrix M according to the following formula, and obtaining a population quality matrix M through normalization;
wherein ,
for the fitness of particle i +.>
Is the optimal value at the t-th iteration, < >>
Is the worst value at the t-th iteration; />
The particle quality after normalization;
s55: updating the gravitational constant G according to the following formula;
wherein ,
is the initial gravitational constant, +.>
Is constant, T is the maximum number of iterations;
s56: calculating Euclidean distance between particles i and j, and calculating attraction force F between particles i and j on the kth dimension at the t-th iteration according to the following ij ；
wherein ,
is gravitational constant, ++>
Is the Euclidean distance between particle i and particle j;
s57: updating the resultant force F and the particle acceleration a suffered by the particle i according to the following formula;
the total force to which the particle i is subjected in the k-th dimension is equal to the random weighted sum of the forces of the other particles:
wherein p is a random parameter, representing a random weight;
the acceleration of the particles is calculated according to the following formula:
s58: updating particle velocity and position according to
wherein ,
is the k-dimensional component of the position of particle i, < >>
Is the k-dimensional component of the velocity of particle i, < >>
、/>
、/>
Is [0,1]Random parameters in->
、/>
For learning factors->
For the current optimal position of the kth dimension individual, < >>
The k-th dimension global optimal position;
by adjusting
、/>
The value of (2) can adjust the degree of influence of individuals and groups in the particle movement process;
s59: calculating a mutation trigger function value, and judging whether the mutation trigger function value meets a mutation condition or not;
the algorithm is improved based on the self-adaptive mutation method, and the mutation trigger function value calculation method comprises the following steps:
wherein ,
for the abrupt trigger value of the ith particle in the t-th iteration, when +.>
When the threshold value is larger than the set threshold value, triggering abrupt change; p is a random parameter, dim is the dimension of the solution,>
is a smaller constant;
s510: performing position uniform mutation
The uniform mutation update particle location formula is as follows:
wherein p is a random parameter ranging between [0,1 ];
s511: and (5) carrying out loop iteration, and outputting a global optimal solution, namely the quality grade of the matched essence.
8. The flavor quality detection method based on the flavor sensor of claim 1, wherein in the step S58
、/>
Calculated according to the following formula:
wherein G is an gravitation constant, T is the current iteration number, and T is the maximum iteration number.
9. A system based on the scent sensor-based fragrance quality detection method of any of claims 1-8, comprising:
an odor sensor array comprising a W1C sensor for sensing aromatic compounds, a W5S sensor for sensing nitrogen oxides, a W3C sensor for sensing ammonia and aromatic compounds, a W6S sensor for sensing hydrogen, a W1W sensor for sensing sulfides, a W2S sensor for sensing ethanol, a W3S sensor for sensing alkanes;
the data preprocessing module is used for preprocessing sensor data based on the one-dimensional convolutional neural network;
the multi-sensor data fusion module is used for fusing the data characteristics of the plurality of sensors, and the fusion method is to splice and fuse the category probability vector and the feature vector subjected to the dimension reduction treatment to obtain the fused features;
the class probability vector is obtained by utilizing original input features to respectively perform feature training based on different classifiers;
and the feature recognition module is used for recognizing the fused data features based on an gravitation search algorithm.
10. A computer readable storage medium having stored thereon program instructions of a scent sensor based fragrance quality detection method, the program instructions of the scent sensor based fragrance quality detection being executable by one or more processors to implement the steps of the scent sensor based fragrance quality detection method of one of claims 1-8.
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