CN114816771B - Multi-channel hybrid cloud computing system - Google Patents
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Abstract
The invention provides a multi-channel hybrid cloud computing system which comprises a data acquisition module, a data sorting module, a data fusion module, a data preprocessing module, a data refining processing module, a private cloud network and a public cloud network. Collecting multi-channel data, performing digital processing on the collected data, and sorting the digital data to obtain different types of processable data forms; performing fusion processing on the received data, performing preprocessing after transforming the received data into processable data, and performing data refining processing after obtaining a transformation data matrix set; and storing the refined data into respective cloud networks for corresponding cloud computing. The invention solves the technical problems of lower efficiency and poorer flexibility in the prior art, and realizes the technical effects of high-efficiency operation and higher flexibility.
Description
Technical Field
The invention relates to the technical field of data processing and cloud computing, in particular to a multi-channel hybrid cloud computing system.
Background
Hybrid cloud computing mixes cloud computing services of private cloud computing and common cloud computing, in recent years, hybrid cloud becomes a mainstream development trend of cloud computing in the future, and the rise of hybrid cloud puts high requirements on multi-cloud management capability, cloud network coordination capability, safety capability and the like.
"201811096061.7" of the invention, "a method, apparatus, and system for hybrid cloud-based computing, the method performed by a first storage manager within a first cloud network, the method comprising: generating a first virtual storage medium corresponding to the mirror image file on the second cloud network; in response to a compute instance on a first cloud network being launched, receiving a data request related to the compute instance forwarded by a first virtual storage medium; and processing the data request by utilizing a second storage manager, wherein the second storage manager is a second virtual storage medium used for managing the storage image file on a second cloud network.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems: the efficiency is low and the flexibility is poor.
Disclosure of Invention
By providing the multi-channel hybrid cloud computing system, the technical problems of low efficiency and poor flexibility in the prior art are solved, and the technical effects of high-efficiency operation and high flexibility are achieved.
The invention provides a multichannel hybrid cloud computing system, which specifically comprises the following technical scheme:
a multi-channel hybrid cloud computing system comprising the following:
the system comprises a data acquisition module, a data sorting module, a data fusion module, a data preprocessing module, a data refining processing module, a private cloud network and a public cloud network;
the data acquisition module is used for acquiring data of each channel, digitizing the acquired data and sending the digitized data to the data sorting module;
the data sorting module is used for sorting the acquired digital data and classifying two different types of data of common private users;
the data fusion module is used for carrying out fusion processing on the multi-channel data, transforming the multi-channel data into a form easy to process and then sending the multi-channel data into the data preprocessing module;
the data preprocessing module is used for preprocessing the data transmitted by the data fusion module and providing a cushion for the data refining processing;
the data refining processing module is used for refining the preprocessed data to provide a basis for hybrid cloud computing;
the private cloud network is used for storing and calculating the refined private data;
the public cloud network is used for storing and calculating the refined public data;
data communication can be carried out between the public cloud network and the private cloud network.
A multi-channel hybrid cloud computing method comprises the following steps:
s1, collecting multi-channel data, carrying out digital processing on the collected data, and sorting the digital data to obtain different types of processable data forms;
s2, performing fusion processing on the received data, performing preprocessing after transforming the data into processable data, and performing data refining processing after obtaining a transformation data matrix set;
and S3, storing the refined data into respective cloud networks for corresponding cloud computing.
Further, the step S1 includes:
distinguishing according to the type of Data by user, collecting private Data and common Data by multi-channel collector, digitizing the collected Data to obtain digitized Data processable by computer, defining the digitized Data as Data matrix Data,
and N represents the total number of data groups,
indicates the data of the ith group,
m represents the number of data in the ith group of data,
representing the jth data in the ith group of data;
when the data is collected in multiple channels, the collected data is numbered according to the private and public properties, and the data is marked by using a label so as to distinguish the private data from the public data.
Further, the step S1 further includes:
sorting the digitized Data matrix Data to obtain a private Data matrix and a public Data matrix, and performing cross coding processing on the two Data, wherein the specific processing is as follows:
for private data
：
For public data
：
Wherein the content of the first and second substances,
the private data is represented by a representation of,
indicating public data, N1 indicating the number of private data groups, M1 indicating the number of private data per group, N2 indicating the number of public data groups, M2 indicating the number of public data per group,
representing the first in a private data matrixThe j-th data in the i arrays,
representing the jth data in the ith array in the public data matrix.
Further, the step S2 includes:
cross-coding private data matrix
Public data matrix
Piecing together to obtain a piecing matrix
Then to the matrix
And calculating the correlation group by group according to the following calculation process:
firstly, sequencing each column vector of the matrix after combination from big to small, and then calculating the sequenced data as follows to obtain a correlation coefficient:
wherein M represents the number of data elements in each column group, the j, s sub-table represents the j, s array, i.e. the j, s column of the transformed matrix,
，
the number of columns representing the j, s,
n represents the number of arrays;
according to the calculated correlation coefficient matrix
Determining the correlation according to the magnitude of the correlation coefficient value of each row in the R, wherein the larger the value is, the larger the correlation is, and then combining the data characteristics to fuse the two groups of data to obtain a new data fusion matrix
。
Further, the step S2 further includes:
the method comprises the steps of utilizing the existing wavelet packet transformation technology to reduce noise of data, firstly carrying out wavelet packet decomposition, namely further decomposing a low-frequency sub-band and a high-frequency sub-band when each level of data is decomposed, calculating an optimal data decomposition path by minimizing a cost function, decomposing original data by the optimal data decomposition path, and realizing preliminary denoising of the data to obtain a data set
；
Carrying out data refining treatment by constructing a refining extraction model, which comprises the following steps:
data represents a preprocessed Data matrix, T represents a feature vector of the preprocessed Data matrix Data, QD represents a feature weight vector corresponding to the preprocessed Data matrix Data, YS represents a constraint matrix for the preprocessed Data matrix Data, and Out represents output of a model, namely the Data matrix after Data refining processing.
Further, the step S3 includes:
sorting the refined data by constructing a sorting processing model, wherein the model comprises the following concrete steps:
wherein the content of the first and second substances,
indicating the data matrix after the refining process, FT indicating the characteristic parameter vector of the data matrix after the refining process, QD indicating the characteristic vector of the data matrix after the refining process,
and expressing model output, namely a private data matrix and a public data matrix.
The invention has at least the following technical effects or advantages:
1. according to the invention, the data acquisition is carried out on the data of each channel by using the multi-channel acquisition device, so that the efficiency of the multi-channel hybrid cloud computing system is improved, the data are marked by using the label during the acquisition, a basis is provided for data sorting, and the flexibility of data processing is improved.
2. According to the invention, private and public data are cross-encoded, so that the data are protected to a certain extent, the accuracy of the data is improved, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
3. According to the method, the correlation coefficient is obtained by utilizing the correlation between each group of data, and the data fusion is carried out after the correlation coefficient is analyzed, so that the data redundancy is reduced, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
4. According to the method, the data are refined through constructing a refining extraction model, the constraint matrix is added according to the specific application environment, and the data matrix is further limited, so that the adaptability of the multi-channel hybrid cloud computing system is improved.
5. According to the method, the sorting processing model is built, the refined data is sorted, the private public data are more accurately distinguished, purer data are obtained, a basis is provided for subsequent cloud computing, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
Drawings
FIG. 1 is a block diagram of a multi-channel hybrid cloud computing system according to the present invention;
fig. 2 is a flowchart of a multi-channel hybrid cloud computing method according to the present invention.
Detailed Description
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
firstly, acquiring multi-channel data, performing digital processing on the acquired data, and sorting the digital data according to labels during acquisition to obtain different types of processable data forms; then, carrying out fusion processing on the received data by utilizing relevant properties, carrying out preprocessing after transforming the received data into easily processed data, obtaining a preprocessing data matrix set, and carrying out data refining processing by constructing a refining extraction model; and finally, sorting the data after the refining treatment by constructing a sorting treatment model, storing the sorted data into respective cloud networks, and carrying out corresponding cloud computing. The data acquisition is carried out on the data of each channel by using the multi-channel acquisition device, so that the efficiency of the multi-channel hybrid cloud computing system is improved, the data are marked by using the tags during the acquisition, a basis is provided for data sorting, and the flexibility of data processing is improved; by cross coding private and public data, the data is protected to a certain extent, the accuracy of the data is improved, and the efficiency of a multi-channel hybrid cloud computing system is further improved; correlation coefficients are obtained by utilizing the correlation among each group of data, and are analyzed and then subjected to data fusion, so that the data redundancy is reduced, and the efficiency of the multi-channel hybrid cloud computing system is further improved; the data are refined through constructing a refining extraction model, a constraint matrix is added according to a specific application environment, and the data matrix is further limited, so that the adaptability of the multi-channel hybrid cloud computing system is improved; by constructing the sorting processing model, the data after refining processing is sorted, private public data are more accurately distinguished, purer data are obtained, a basis is provided for subsequent cloud computing, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 1, a multi-channel hybrid cloud computing system specifically includes the following components:
the system comprises a data acquisition module, a data sorting module, a data fusion module, a data preprocessing module, a data refining processing module, a private cloud network and a public cloud network;
the data acquisition module is used for acquiring data of each channel, digitizing the acquired data and sending the digitized data to the data sorting module;
the data sorting module is used for sorting the acquired digital data and classifying two different types of data of common private users;
the data fusion module is used for carrying out fusion processing on the multi-channel data, transforming the multi-channel data into a form easy to process and then sending the multi-channel data into the data preprocessing module;
the data preprocessing module is used for preprocessing the data transmitted by the data fusion module and providing a cushion for data refining processing;
the data refining processing module is used for refining the preprocessed data and providing a basis for hybrid cloud computing;
the private cloud network is used for storing, calculating and processing the refined private data;
the public cloud network is used for storing and calculating the refined public data;
preferably, data communication can be carried out between the public cloud network and the private cloud network.
Referring to fig. 2, a multi-channel hybrid cloud computing method specifically includes the following steps:
s1, firstly, acquiring multi-channel data, performing digital processing on the acquired data, and sorting the digital data to obtain different types of processable data forms;
s11, multi-channel acquisition is carried out by utilizing a multi-channel data acquisition unit according to the private type and the public type of the data, and the acquired data are subjected to digital processing;
distinguishing according to the type of Data, collecting private Data and common Data with multi-channel collector, digitizing the collected Data to obtain digitized Data processable by computer, defining the digitized Data as Data matrix Data,
and N represents the total number of data groups,
indicates the data of the ith group,
m represents the number of data in the ith group of data,
indicating the jth data in the ith set of data.
Preferably, when the data is collected in multiple channels, the collected data is numbered according to private and public properties, and the data is marked by using a label so as to distinguish the private data from the public data and provide a basis for sorting the data.
According to the invention, the data acquisition is carried out on the data of each channel by using the multi-channel acquisition device, so that the efficiency of the multi-channel hybrid cloud computing system is improved, the data are marked by using the label during the acquisition, a basis is provided for data sorting, and the flexibility of data processing is improved.
S12, sorting the digitized data to obtain different types of processable data forms;
sorting the digitized Data matrix Data to obtain a private Data matrix and a public Data matrix, which specifically comprises the following steps:
wherein the content of the first and second substances,
the private data is represented by a representation of,
indicating public data, N1 indicating the number of private data sets, M1 indicating the number of private data per set of data, N2 indicating the number of public data sets, M2 indicating the number of public data per set of data,
representing the jth data in the ith array in the private data matrix,
representing the jth data in the ith array in the public data matrix.
Respectively to private data
Public data
Performing cross coding treatment, specifically comprising the following steps:
for private data
：
For public data
：
In the present invention, M1 is considered to be<N1，M2<N2, as a specific example, when M1=4, L1=6, the corresponding private data matrix
Is represented as follows:
according to the invention, private and public data are cross-encoded, so that the data are protected to a certain extent, the accuracy of the data is improved, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
S2, then, carrying out fusion processing on the received data, carrying out preprocessing after transforming the received data into easily processed data, obtaining a preprocessed data matrix set, and carrying out data refining processing;
s21, fusing the private and public data subjected to cross coding to obtain easily processed data;
the private data matrix cross-encoded in the step S1
Public data matrix
Performing fusion processing to obtain easily processed data, specifically fusing as follows:
fusing data according to the correlation between the data, and combining the matrixes
And calculating the correlation group by group according to the following calculation process:
firstly, sequencing vectors in each column of the matrix after combination from large to small to obtain a matrix:
and calculating the sorted data as follows to obtain a correlation coefficient:
wherein M represents the number of data elements in each column group, the j, s sub-table represents the j, s array, i.e. the j, s column of the transformed matrix,
，
the number of columns representing the j, s,
。
according to the calculated correlation coefficient matrix:
and determining the correlation according to the magnitude of the correlation coefficient value of each row, wherein the larger the value is, the larger the correlation is, and then combining the data characteristics to fuse the two groups of data to obtain a new data fusion matrix:
wherein, X represents the number of arrays in the fusion matrix, and M represents the number of data elements in each data.
According to the method, the correlation coefficient is obtained by utilizing the correlation between each group of data, and the data fusion is carried out after the correlation coefficient is analyzed, so that the data redundancy is reduced, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
S22, denoising preprocessing is carried out on the data easy to process to obtain a transformation data matrix set;
as a specific embodiment, the present invention utilizes the existing wavelet packet transform technology to perform denoising on data, and first performs wavelet packet decomposition (an optimal subband number structure, which is a further optimization of wavelet transform), that is, when decomposing data at each level, the present invention performs further decomposition on high frequency subbands as well as low frequency subbands. And finally, calculating an optimal data decomposition path by minimizing a cost function, decomposing the original data according to the optimal data decomposition path, and realizing preliminary denoising of the data to obtain a data set
，
S23, refining the preprocessed data matrix to obtain data convenient to calculate;
carrying out data refining treatment by constructing a refining extraction model, which comprises the following steps:
data represents a preprocessed Data matrix, T represents a characteristic parameter vector of the preprocessed Data matrix Data, QD represents a characteristic vector corresponding to the preprocessed Data matrix Data, YS represents a constraint matrix for the preprocessed Data matrix Data, and Out represents output of a model, namely the Data matrix after Data refining processing.
The characteristic parameter vector T comprises data sources (different instruments and equipment and other relevant influence factors), data physical meanings, data purposes, relevant characteristics, difference characteristics and other relevant basic characteristics.
The constraint matrix represents the constraint conditions between the data types when the hybrid cloud computing is performed, for example: the constraints between the private data and the public data, the constraints of the private data from different data sources, the constraints of the public data from different data sources and other related data constraints,
according to the method, the data are refined through constructing a refining extraction model, the constraint matrix is added according to the specific application environment, and the data matrix is further limited, so that the adaptability of the multi-channel hybrid cloud computing system is improved.
And S3, finally, storing the refined data into respective cloud networks for corresponding cloud computing.
S31, sorting the refined data to obtain processed private data and public data;
constructing a sorting processing model, which comprises the following steps:
wherein the content of the first and second substances,
indicating the data matrix after the refining process, FT indicating the characteristic parameter vector of the data matrix after the refining process, QD indicating the characteristic vector of the data matrix after the refining process,
and expressing model output, namely a private data matrix and a public data matrix.
The characteristic parameter vector representation comprises data sources (marked during acquisition), data physical meanings, data purposes, relevant characteristics, difference characteristics and other relevant basic characteristics,
according to the method, the sorting processing model is built, the refined data is sorted, the private public data are more accurately distinguished, purer data are obtained, a basis is provided for subsequent cloud computing, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
And S32, storing the processed private and public data into respective cloud networks for corresponding cloud computing.
After the refined data are sorted through the sorting processing model, different types of data are stored in respective cloud network memories, and corresponding cloud computing processing is further performed on the data.
In summary, the multi-channel hybrid cloud computing system of the present invention is completed.
Effect investigation:
the technical scheme of the invention can effectively solve the technical problems of low efficiency and poor flexibility, and the system or the method improves the efficiency of the multi-channel hybrid cloud computing system by a series of effect investigation and simultaneously carrying out data acquisition on each channel data by using the multi-channel acquisition device, provides a basis for data sorting by using a label to mark the data during acquisition, and improves the flexibility of data processing; by cross coding private and public data, the data is protected to a certain extent, the accuracy of the data is improved, and the efficiency of a multi-channel hybrid cloud computing system is further improved; correlation coefficients are obtained by utilizing the correlation among each group of data, and are analyzed and then subjected to data fusion, so that the data redundancy is reduced, and the efficiency of the multi-channel hybrid cloud computing system is further improved; the data are refined through constructing a refining extraction model, a constraint matrix is added according to a specific application environment, and the data matrix is further limited, so that the adaptability of the multi-channel hybrid cloud computing system is improved; by constructing the sorting processing model, the data after refining processing is sorted, private public data are distinguished more accurately, purer data are obtained, basis is provided for subsequent cloud computing, and the efficiency of the multi-channel hybrid cloud computing system is further improved.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (2)
1. A multi-channel hybrid cloud computing system, comprising:
the system comprises a data acquisition module, a data sorting module, a data fusion module, a data preprocessing module, a data refining processing module, a private cloud network and a public cloud network;
the data acquisition module is used for acquiring data of each channel, digitizing the acquired data and sending the digitized data to the data sorting module;
distinguishing data types according to users, respectively collecting private data and common data by using a multi-channel collector, digitizing the collected data to obtain digitized data which can be processed by a computer, and defining the digitized data as a data matrixData，
，NWhich represents the number of total sets of data,
is shown asiThe data of the group is composed of data,
，Mis shown asiThe number of data of the group data,
is shown asiIn group datajA piece of data;
when data is acquired in a multi-channel mode, the acquired data is numbered according to private and public properties, and the data is marked by using a label so as to distinguish private data from public data;
the data sorting module is used for sorting the collected digital data and classifying the data of two different types which are shared privately;
after the digitization of the data matrixDataAfter sorting, the product is obtainedAnd performing cross coding processing on the private data matrix and the public data matrix, wherein the cross coding processing is specifically as follows:
for private data
：
For public data
：
Wherein the content of the first and second substances,
the private data is represented by a representation of,
it is shown that the public data is,N1indicating the number of private data sets,M1indicating the number of private data per group of data,N2indicating the number of the common data sets,M2the number of the common data in each group is shown,
representing the first in a private data matrixiFirst in a number arrayjThe number of the data is one,
representing the first in a public data matrixiFirst in a number arrayjA piece of data;
the data fusion module is used for carrying out fusion processing on the multi-channel data, transforming the multi-channel data into a form easy to process and then sending the multi-channel data into the data preprocessing module;
cross-coding private data matrix
Public data matrix
Piecing together to obtain a piecing matrix
Then to the matrix
And calculating the correlation group by group according to the following calculation process:
firstly, sequencing vectors of each column of the matrix after combination from large to small, and then calculating the sequenced data as follows to obtain a correlation coefficient:
wherein the content of the first and second substances,Mindicating the number of data elements in each column group,j，srespectively representj，sNumber of arrays, i.e. of transformed matricesj，sThe columns of the image data are,
，
denotes the firstj，sThe number of columns is set as follows,
，Nrepresenting the number of the arrays;
according to the calculated correlation coefficient matrix
Determining the correlation according to the magnitude of the correlation coefficient value of each row in the R, wherein the larger the value is, the larger the correlation is, and then combining the data characteristics to fuse the two groups of data to obtain a new data fusion matrix
；
The data preprocessing module is used for preprocessing the data transmitted by the data fusion module and providing a cushion for the data refining processing;
the method comprises the steps of utilizing the existing wavelet packet transformation technology to reduce noise of data, firstly carrying out wavelet packet decomposition, namely further decomposing low-frequency sub-bands and high-frequency sub-bands when each level of data is decomposed, calculating an optimal data decomposition path by minimizing a cost function, decomposing original data according to the optimal data decomposition path, preliminarily denoising the data, and obtaining a data set
；
The data refining processing module is used for refining the preprocessed data to provide a basis for hybrid cloud computing;
carrying out data refining treatment by constructing a refining extraction model, which comprises the following steps:
wherein the content of the first and second substances,Datarepresenting the pre-processed data matrix and the pre-processed data matrix,Trepresenting a preprocessed data matrixDataIs determined by the feature vector of (a),QDrepresenting a preprocessed data matrixDataThe corresponding feature weight vector is then used to determine,YSrepresenting pre-processing of dataDataThe constraint matrix of (a) is defined,Outrepresenting the output of the model, namely a data matrix after data refining processing;
sorting the refined data by constructing a sorting processing model, wherein the model comprises the following specific steps:
wherein the content of the first and second substances,
a data matrix after the refining process is represented,FTa characteristic parameter vector representing the data matrix after refining,QDa feature vector representing the data matrix after the refining process,
representing model output, namely a private data matrix and a public data matrix;
the private cloud network is used for storing, calculating and processing the refined private data;
the public cloud network is used for storing, calculating and processing the refined public data;
data communication can be carried out between the public cloud network and the private cloud network.
2. A multi-channel hybrid cloud computing method is characterized by comprising the following steps:
s1, collecting multi-channel data, performing digital processing on the collected data, and sorting the digital data to obtain different types of processable data forms;
s2, performing fusion processing on the received data, performing preprocessing after transforming the data into processable data, and performing data refining processing after obtaining a transformation data matrix set;
s3, storing the refined data into respective cloud networks for corresponding cloud computing;
the step S1 includes:
the data types are distinguished according to users, private data and common data are respectively collected by a multi-channel collector, the collected data are digitized,obtaining digitized Data which can be processed by a computer, defining the digitized Data as a Data matrix Data,
and N represents the total number of data groups,
indicates the data of the ith group,
m represents the number of data in the ith group of data,
representing the jth data in the ith group of data;
when data is acquired in a multi-channel mode, the acquired data is numbered according to private and public properties, and the data is marked by using a label so as to distinguish private data from public data;
sorting the digitized Data matrix Data to obtain a private Data matrix and a public Data matrix, and performing cross coding processing on the two Data, wherein the specific processing is as follows:
for private data
：
For public data
：
Wherein, the first and the second end of the pipe are connected with each other,
the private data is represented by a representation of,
indicating public data, N1 indicating the number of private data groups, M1 indicating the number of private data per group, N2 indicating the number of public data groups, M2 indicating the number of public data per group,
representing the jth data in the ith array in the private data matrix,
representing the jth data in the ith array in the public data matrix;
the step S2 includes:
cross-coding private data matrix
Public data matrix
Piecing together to obtain a piecing matrix
Then to the matrix
And calculating the correlation group by group, wherein the calculation process is as follows:
firstly, sequencing each column vector of the matrix after combination from big to small, and then calculating the sequenced data as follows to obtain a correlation coefficient:
wherein M represents the number of data elements in each column group, j and s respectively represent the j and s-th arrays, namely the j and s-th columns of the transformed matrix,
，
the number of columns representing the j, s,
n represents the number of arrays;
according to the calculated correlation coefficient matrix
Determining the correlation according to the magnitude of the correlation coefficient value of each row in the R, wherein the larger the value is, the larger the correlation is, and then combining the data characteristics to fuse the two groups of data to obtain a new data fusion matrix
；
The method comprises the steps of utilizing the existing wavelet packet transformation technology to reduce noise of data, firstly carrying out wavelet packet decomposition, namely further decomposing low-frequency sub-bands and high-frequency sub-bands when each level of data is decomposed, calculating an optimal data decomposition path by minimizing a cost function, decomposing original data according to the optimal data decomposition path, preliminarily denoising the data, and obtaining a data set
；
Carrying out data refining treatment by constructing a refining extraction model, which comprises the following steps:
the Data represents a preprocessed Data matrix, T represents a feature vector of the preprocessed Data matrix Data, QD represents a feature weight vector corresponding to the preprocessed Data matrix Data, YS represents a constraint matrix for the preprocessed Data matrix Data, and Out represents the output of a model, namely the Data matrix after Data refining processing;
the step S3 includes:
sorting the refined data by constructing a sorting processing model, wherein the model comprises the following specific steps:
wherein the content of the first and second substances,
indicating the data matrix after the refining process, FT indicating the characteristic parameter vector of the data matrix after the refining process, QD indicating the characteristic vector of the data matrix after the refining process,
and expressing model output, namely a private data matrix and a public data matrix.
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