CN116522105A - Method, device, equipment and medium for integrally constructing data based on cloud computing - Google Patents
Method, device, equipment and medium for integrally constructing data based on cloud computing Download PDFInfo
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Abstract
The invention relates to the technical field of data management, and discloses a method for integrally constructing data based on cloud computing, which comprises the following steps: acquiring cloud computing data to be processed, carrying out attribute analysis on the cloud computing data to obtain data attributes, and carrying out feature extraction on the data attributes to obtain feature attributes; calculating the relevance of each data in the cloud computing data according to the characteristic attribute to obtain data relevance, and carrying out data merging on the cloud computing data according to the data relevance to obtain merged data; identifying a data type corresponding to the merged data, configuring data nodes of the merged data according to the data type, and calculating the complexity of each data in the merged data to obtain the data complexity; according to the complexity of the data, setting the data level of each data in the merged data, and combining the data nodes and the data levels to construct an integrated platform of the merged data. The invention aims to improve the rationality of the data integrated construction of cloud computing.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a method, a device, equipment and a medium for integrally constructing data based on cloud computing.
Background
The cloud computing is a result of mixed evolution and jump of computer technologies such as distributed computing, utility computing, parallel computing, network storage, hot backup redundancy, virtualization and the like.
The existing method for integrally managing the cloud computing data mainly comprises the steps of intensively storing all the data and giving corresponding labels to the data so as to facilitate the subsequent integrated management of the data, but the method does not comprehensively consider the data, such as the relevance among the data, the complex condition of the data and the like, and the management difficulty is increased during the subsequent integrated management of the data, so that the efficiency of the integrated management of the cloud computing data is low, and therefore, a method capable of improving the rationality of the integrated construction of the cloud computing data is needed.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for integrally constructing data based on cloud computing, and mainly aims to improve the rationality of integrally constructing the data of the cloud computing.
In order to achieve the above purpose, the method for integrally constructing data based on cloud computing provided by the invention comprises the following steps:
acquiring cloud computing data to be processed, performing attribute analysis on the cloud computing data to obtain data attributes, and performing feature extraction on the data attributes to obtain feature attributes;
according to the characteristic attribute, calculating the relevance of each data in the cloud computing data to obtain data relevance, and according to the data relevance, carrying out data merging on the cloud computing data to obtain merged data;
identifying a data type corresponding to the merged data, configuring a data node of the merged data according to the data type, and calculating the complexity of each data in the merged data to obtain the data complexity;
and setting the data level of each data in the merged data according to the data complexity, and combining the data nodes and the data level to construct an integrated platform of the merged data.
Optionally, the extracting the features of the data attribute to obtain a feature attribute includes:
preprocessing the data attributes to obtain target attributes, and linearly converting each attribute in the target attributes to obtain linear values;
Performing fast Fourier transform on the target attribute according to the linear value to obtain attribute signals, and extracting time domain features and frequency domain features of each signal in the attribute signals;
and carrying out feature combination on the time domain features and the frequency domain features to obtain target features, and taking the target features as feature attributes of the data attributes.
Optionally, the feature combining the time domain feature and the frequency domain feature to obtain a target feature includes:
respectively constructing feature matrixes of the time domain features and the frequency domain features to obtain a first feature matrix and a second feature matrix;
and carrying out weighted summation on the first feature matrix and the second feature matrix through the following formula to obtain a target feature matrix:
wherein B represents a target feature matrix, S represents a sigmoid function, a represents an initial matrix in the first feature matrix and the second feature matrix, z represents the total number of the first feature matrix and the second feature matrix, D represents the average value of each matrix in the first feature matrix and the second feature matrix, and D a Representing the average value of the a-th matrix, e (a, z) representing the range to which the matrix belongs;
and calculating a target feature value corresponding to the target feature matrix, and obtaining a target feature according to the target feature value.
Optionally, calculating the relevance of each data in the cloud computing data according to the feature attribute to obtain a data relevance, including:
extracting a characteristic label of each data in the cloud computing data according to the characteristic attribute;
constructing a feature vector of each tag in the feature tags;
and calculating the relevance of each data in the cloud computing data according to the feature vector to obtain the data relevance.
Optionally, calculating the relevance of each data in the cloud computing data according to the feature vector to obtain a data relevance, including:
the relevance of each data in the cloud computing data is calculated by the following formula:
wherein C represents the data relevance of each data in the cloud computing data, E represents the dimension coefficient of the cloud computing data, Y represents the data quantity value of the cloud computing data, j represents the starting value of the cloud computing data, F j Corresponding feature vector lnF representing jth cloud computing data j The j-th cloud calculates the logarithmic value of the eigenvector of the data, F j+1 Corresponding eigenvectors, lnF, representing the j+1th cloud computing data j+1 The logarithmic value of the feature vector representing the j+1th cloud computing data, max () represents the maximum value of the logarithmic difference value, min () represents the minimum value of the logarithmic difference value, and ω represents the correlation coefficient of the cloud computing data.
Optionally, the calculating the complexity of each data in the merged data, to obtain the data complexity includes:
calculating the weight of each data in the combined data to obtain a data weight value, and carrying out data sequencing on the combined data according to the data weight value to obtain a data sequence;
performing numerical processing on each data in the merged data to obtain a data value, and constructing a scatter diagram corresponding to each data according to the data value and the data sequence;
calculating the complexity of each graph in the scatter graph to obtain image complexity, and determining the data complexity of the combined data according to the image complexity.
Optionally, the calculating the complexity of each graph in the scatter plot, to obtain the image complexity, includes:
the complexity of each plot in the scatter plot is calculated by the following formula:
where G represents the image complexity, H, of each of the scatter plots k Representing the length of a graphics bus corresponding to a kth scatter diagram in the scatter diagrams, M k Represents the coordinate mean value of the x-axis corresponding to the kth scatter diagram, N k Represents the coordinate mean value of the y-axis corresponding to the kth scattergram, and ∈cd represents the integral of the coordinate mean difference between the x-axis and the y-axis corresponding to the kth scattergram.
In order to solve the above problems, the present invention further provides a device for data integration construction based on cloud computing, the device comprising:
the feature extraction module is used for acquiring cloud computing data to be processed, carrying out attribute analysis on the cloud computing data to obtain data attributes, and carrying out feature extraction on the data attributes to obtain feature attributes;
the data merging module is used for calculating the relevance of each data in the cloud computing data according to the characteristic attribute to obtain data relevance, and carrying out data merging on the cloud computing data according to the data relevance to obtain merged data;
the node configuration module is used for identifying the data type corresponding to the combined data, configuring the data nodes of the combined data according to the data type, and calculating the complexity of each data in the combined data to obtain the data complexity;
and the platform construction module is used for setting the data level of each data in the merged data according to the data complexity and constructing an integrated platform of the merged data by combining the data nodes and the data level.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
At least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of cloud computing-based data integration construction described above.
In order to solve the above-mentioned problems, the present invention further provides a computer readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the method for integrally building data based on cloud computing.
According to the method, the correlation degree of each data in the cloud computing data can be known by calculating the correlation of each data in the cloud computing data according to the characteristic attribute, so that the guarantee is provided for the subsequent data merging of the cloud computing data, wherein the data type corresponding to the merged data is identified, the data nodes of the merged data are configured according to the data type, and the precondition is provided for the subsequent construction of an integrated platform of the merged data; in addition, the data level of each data in the merged data is set according to the data complexity, so that the level of each data in the merged data can be obtained, and the merged data can be subjected to data hierarchical management later. Therefore, the method, the device, the equipment and the medium for integrally constructing the data based on the cloud computing can improve the rationality of integrally constructing the data of the cloud computing.
Drawings
Fig. 1 is a flow chart of a method for integrally constructing data based on cloud computing according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a device built by integrating data based on cloud computing according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the method for integrally building data based on cloud computing according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a method for integrally constructing data based on cloud computing. In the embodiment of the present application, the execution body of the method for integrally constructing data based on cloud computing includes, but is not limited to, at least one of a server, a terminal, and an electronic device capable of being configured to execute the method provided in the embodiment of the present application. In other words, the method for integrally building data based on cloud computing can be performed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for integrally constructing data based on cloud computing according to an embodiment of the present invention is shown. In this embodiment, the method for integrally constructing data based on cloud computing includes steps S1 to S4:
s1, acquiring cloud computing data to be processed, performing attribute analysis on the cloud computing data to obtain data attributes, and performing feature extraction on the data attributes to obtain feature attributes.
According to the cloud computing data processing method and device, the cloud computing data to be processed are obtained, and attribute analysis is carried out on the cloud computing data, so that relevant attribute information of the cloud computing data can be obtained, and further guarantee is provided for extraction of subsequent characteristic attributes.
The cloud computing data is obtained by decomposing a huge data computing processing program into countless small programs, then processing and analyzing the small programs through a system formed by a plurality of servers, wherein the data attribute is the numerical value of the cloud computing data and attribute information such as physics and the like, and further, the attribute analysis of the cloud computing data can be realized through an attribute analysis function, such as a map state function.
According to the method and the device for extracting the characteristics of the cloud computing data, the characteristic extraction is carried out on the data attributes, so that the data with the representativeness in the data attributes can be obtained, and the relevance of each data in the cloud computing data can be conveniently calculated later.
As an embodiment of the present invention, the feature extracting the data attribute to obtain a feature attribute includes: preprocessing the data attribute to obtain a target attribute, performing linear conversion on each attribute in the target attribute to obtain a linear value, performing fast Fourier transform on the target attribute according to the linear value to obtain an attribute signal, extracting the time domain feature and the frequency domain feature of each signal in the attribute signal, performing feature combination on the time domain feature and the frequency domain feature to obtain a target feature, and taking the target feature as the feature attribute of the data attribute.
The target attribute is obtained by processing redundant, repeated and useless attributes in the data attribute, the linear value is a linear value corresponding to each attribute in the target attribute, the attribute signal is a signal curve expression form corresponding to each attribute in the target attribute, the time domain feature is a feature relation between the attribute signal and time, the frequency domain feature is a frequency feature in the attribute signal, and the target feature is a feature obtained by combining the time domain feature and the frequency domain feature.
Further, preprocessing the data attribute may be achieved by a principal component analysis method, performing linear transformation on each attribute in the target attribute may be achieved by a linear function, performing fast fourier transformation on the target attribute may be achieved by a fourier transformation algorithm, such as a fourier transformation FFT algorithm, and time domain features and frequency domain features of each signal in the attribute signal may be obtained by a feature extraction algorithm, such as a HOG feature extraction algorithm.
As an optional embodiment of the present invention, the feature combining the time domain feature and the frequency domain feature to obtain a target feature includes: and respectively constructing feature matrixes of the time domain features and the frequency domain features to obtain a first feature matrix and a second feature matrix, carrying out weighted summation on the first feature matrix and the second feature matrix to obtain a target feature matrix, calculating a target feature value corresponding to the target feature matrix, and obtaining a target feature according to the target feature value.
The first feature matrix and the second feature matrix are square matrix expression forms corresponding to the time domain features and the frequency domain features respectively, the target feature matrix is a matrix obtained by weighted summation of the first feature matrix and the second feature matrix, and the target feature value is a numerical value corresponding to the target feature matrix.
Further, the feature matrix respectively constructing the time domain feature and the frequency domain feature can be realized through a matrix constructing function, the matrix constructing function is compiled by a script language, and the calculation of the target feature value corresponding to the target feature matrix can be realized through an iterative algorithm.
Further, as an optional embodiment of the present invention, performing weighted summation on the first feature matrix and the second feature matrix to obtain a target feature matrix, including:
the first feature matrix and the second feature matrix are weighted and summed by the following formula:
wherein B represents a target feature matrix, S represents a sigmoid function, a represents an initial matrix in the first feature matrix and the second feature matrix, z represents the total number of the first feature matrix and the second feature matrix, D represents the average value of each matrix in the first feature matrix and the second feature matrix, and D a Represents the average value of the a-th matrix and e (a, z) represents the range to which the matrix belongs.
S2, calculating the relevance of each data in the cloud computing data according to the characteristic attribute to obtain data relevance, and carrying out data combination on the cloud computing data according to the data relevance to obtain combined data.
According to the method and the device, the relevance of each data in the cloud computing data is calculated according to the characteristic attribute, so that the relevance degree of each data in the cloud computing data can be known, and further guarantee is provided for the subsequent data merging of the cloud computing data, wherein the data relevance represents the relevance degree of each data in the cloud computing data.
According to one embodiment of the present invention, the calculating the relevance of each data in the cloud computing data according to the feature attribute, to obtain the data relevance includes: and extracting a characteristic label of each data in the cloud computing data according to the characteristic attribute, constructing a characteristic vector of each label in the characteristic label, and computing the relevance of each data in the cloud computing data according to the characteristic vector to obtain the data relevance.
The feature labels are identification information corresponding to each attribute in the feature attributes, the feature vectors are vector expression forms corresponding to the feature labels, further, feature labels of each data in the cloud computing data can be extracted through a label extractor, and feature vectors of each label in the feature labels can be constructed through a Word2vec algorithm.
Further, as an optional embodiment of the present invention, the calculating, according to the feature vector, a relevance of each data in the cloud computing data to obtain a data relevance includes:
the relevance of each data in the cloud computing data is calculated by the following formula:
wherein C represents the data relevance of each data in the cloud computing data, E represents the dimension coefficient of the cloud computing data, Y represents the data quantity value of the cloud computing data, j represents the starting value of the cloud computing data, F j Corresponding feature vector lnF representing jth cloud computing data j The j-th cloud calculates the logarithmic value of the eigenvector of the data, F j+1 Corresponding eigenvectors, lnF, representing the j+1th cloud computing data j+1 The logarithmic value of the feature vector representing the j+1th cloud computing data, max () represents the maximum value of the logarithmic difference value, min () represents the minimum value of the logarithmic difference value, and ω represents the correlation coefficient of the cloud computing data.
According to the method, the cloud computing data are subjected to data combination according to the data relevance, so that the data in the cloud computing data can be combined together, and the data can be searched quickly later, wherein the combined data are obtained after the cloud computing data are combined according to the data relevance, and further, the data combination of the cloud computing data can be realized through a combination algorithm which is compiled by Java language.
S3, identifying a data type corresponding to the merged data, configuring data nodes of the merged data according to the data type, and calculating the complexity of each data in the merged data to obtain the data complexity.
The invention provides a precondition for the subsequent construction of an integrated platform of the merged data by identifying the data type corresponding to the merged data and configuring the data node of the merged data according to the data type, wherein the data type is the type corresponding to the merged data, the data node is the connection point between the data in the merged data, further, the identification of the data type corresponding to the merged data can be identified by a typeof function, and the configuration of the data node of the merged data can be realized by a layout algorithm.
The complexity of each data in the merged data is calculated, so that the complexity of each data in the merged data can be known, and convenience is provided for the subsequent setting of the data level of each data in the merged data, wherein the data complexity represents the complexity of each data in the merged data.
As an embodiment of the present invention, the calculating the complexity of each data in the merged data, to obtain the data complexity includes: calculating the weight of each data in the merged data to obtain a data weight value, carrying out data sequencing on the merged data according to the data weight value to obtain a data sequence, carrying out numerical processing on each data in the merged data to obtain a data value, constructing a scatter diagram corresponding to each data according to the data value and the data sequence, calculating the complexity of each diagram in the scatter diagram to obtain an image complexity, and determining the data complexity of the merged data according to the image complexity.
The data weight value represents the importance degree of each data in the merged data, the data sequence is the order obtained by ordering each data in the merged data according to the data weight value, the data value is the numerical expression form of each data in the merged data, the scatter diagram is a scatter diagram image corresponding to each data in the merged data in a coordinate system, and the image complexity represents the complexity degree of the scatter diagram.
Further, as an optional embodiment of the present invention, calculating the weight of each data in the merged data may be implemented by a fuzzy hierarchy method, performing data sorting on the merged data may be implemented by an bubbling sorting algorithm, performing a numerical processing on each data in the merged data may be implemented by a differential equation, and constructing a scatter diagram corresponding to each data may be implemented by a drawing tool, such as a visual tool.
As an optional embodiment of the present invention, the calculating the complexity of each map in the scatter diagram, to obtain the image complexity includes:
the complexity of each plot in the scatter plot is calculated by the following formula:
Where G represents the image complexity, H, of each of the scatter plots k Representing the length of a graphics bus corresponding to a kth scatter diagram in the scatter diagrams, M k Represents the coordinate mean value of the x-axis corresponding to the kth scatter diagram, N k Represents the coordinate mean value of the y-axis corresponding to the kth scattergram, and ∈cd represents the integral of the coordinate mean difference between the x-axis and the y-axis corresponding to the kth scattergram.
S4, setting the data level of each data in the merged data according to the data complexity, and combining the data nodes and the data levels to construct an integrated platform of the merged data.
According to the data complexity, the data level of each data in the merged data is set so as to obtain the level of each data in the merged data, and then the merged data can be subjected to data hierarchical management, wherein the data level is the level of each data in the merged data, and further, the data level of each data in the merged data can be set according to the value corresponding to the data complexity.
According to the invention, the integrated platform of the merged data is constructed by combining the data nodes and the data levels, the integrated platform can be used for conveniently and comprehensively managing the merged data, and related data can be quickly scheduled later, wherein the integrated platform is an integrated management platform corresponding to the merged data.
As one embodiment of the present invention, the integrating platform for combining the data node and the data level to construct the merged data includes: and creating a data link of each data in the merged data by combining the data nodes and the data level, acquiring a data code corresponding to each data in the merged data, identifying the data capacity of each data, and constructing an integrated platform of the merged data by combining the data link, the data code and the data capacity.
The data link is a physical connection line between each data in the merged data, the data code is an initial computer language corresponding to each data, and the data capacity is a data memory of each data.
Further, creating a data link of each data in the merged data may be implemented by a link aggregation method, acquiring a data code corresponding to each data in the merged data may be implemented by a crawler, and identifying a data capacity of each data may be implemented by a capacity identifying tool, where the capacity identifying tool is programmed by a C language, and an integrated platform for constructing the merged data may be implemented by a platform web server.
According to the method, the correlation degree of each data in the cloud computing data can be known by calculating the correlation of each data in the cloud computing data according to the characteristic attribute, so that the guarantee is provided for the subsequent data merging of the cloud computing data, wherein the data type corresponding to the merged data is identified, the data nodes of the merged data are configured according to the data type, and the precondition is provided for the subsequent construction of an integrated platform of the merged data; in addition, the data level of each data in the merged data is set according to the data complexity, so that the level of each data in the merged data can be obtained, and the merged data can be subjected to data hierarchical management later. Therefore, the method for integrally constructing the data based on the cloud computing can improve the rationality of integrally constructing the data of the cloud computing.
Fig. 2 is a functional block diagram of a device integrally constructed based on cloud computing data according to an embodiment of the present invention.
The cloud computing-based data integrated construction device 100 can be installed in electronic equipment. Depending on the implementation function, the device 100 built by integrating data based on cloud computing may include a feature extraction module 101, a data merging module 102, a node configuration module 103, and a platform construction module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the feature extraction module 101 is configured to obtain cloud computing data to be processed, perform attribute analysis on the cloud computing data to obtain a data attribute, and perform feature extraction on the data attribute to obtain a feature attribute;
the data merging module 102 is configured to calculate, according to the feature attribute, an association of each data in the cloud computing data to obtain a data association, and perform data merging on the cloud computing data according to the data association to obtain merged data;
The node configuration module 103 is configured to identify a data type corresponding to the merged data, configure a data node of the merged data according to the data type, and calculate complexity of each data in the merged data to obtain data complexity;
the platform construction module 104 is configured to set a data level of each data in the merged data according to the data complexity, and combine the data node and the data level to construct an integrated platform of the merged data.
In detail, each module in the cloud computing-based data integrated construction device 100 in the embodiment of the present application adopts the same technical means as the cloud computing-based data integrated construction method described in fig. 1 and can produce the same technical effects, which are not described herein.
Fig. 3 is a schematic structural diagram of an electronic device 1 according to an embodiment of the present invention for implementing a method for integrally building data based on cloud computing.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a method program built integrally based on cloud computing data.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a method program or the like for integrally constructing data based on cloud computing), and invokes data stored in the memory 11 to perform various functions of the electronic device and process the data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in an electronic device and various data, such as code of a method program built by integrating data based on cloud computing, but also temporarily store data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The method program of integrated construction of cloud computing based data stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, it can be implemented:
Acquiring cloud computing data to be processed, performing attribute analysis on the cloud computing data to obtain data attributes, and performing feature extraction on the data attributes to obtain feature attributes;
according to the characteristic attribute, calculating the relevance of each data in the cloud computing data to obtain data relevance, and according to the data relevance, carrying out data merging on the cloud computing data to obtain merged data;
identifying a data type corresponding to the merged data, configuring a data node of the merged data according to the data type, and calculating the complexity of each data in the merged data to obtain the data complexity;
and setting the data level of each data in the merged data according to the data complexity, and combining the data nodes and the data level to construct an integrated platform of the merged data.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring cloud computing data to be processed, performing attribute analysis on the cloud computing data to obtain data attributes, and performing feature extraction on the data attributes to obtain feature attributes;
according to the characteristic attribute, calculating the relevance of each data in the cloud computing data to obtain data relevance, and according to the data relevance, carrying out data merging on the cloud computing data to obtain merged data;
identifying a data type corresponding to the merged data, configuring a data node of the merged data according to the data type, and calculating the complexity of each data in the merged data to obtain the data complexity;
and setting the data level of each data in the merged data according to the data complexity, and combining the data nodes and the data level to construct an integrated platform of the merged data.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for integrally building data based on cloud computing, the method comprising:
Acquiring cloud computing data to be processed, performing attribute analysis on the cloud computing data to obtain data attributes, and performing feature extraction on the data attributes to obtain feature attributes;
according to the characteristic attribute, calculating the relevance of each data in the cloud computing data to obtain data relevance, and according to the data relevance, carrying out data merging on the cloud computing data to obtain merged data;
identifying a data type corresponding to the merged data, configuring a data node of the merged data according to the data type, and calculating the complexity of each data in the merged data to obtain the data complexity;
and setting the data level of each data in the merged data according to the data complexity, and combining the data nodes and the data level to construct an integrated platform of the merged data.
2. The method for integrally building data based on cloud computing as claimed in claim 1, wherein said feature extracting said data attribute to obtain a feature attribute comprises:
preprocessing the data attributes to obtain target attributes, and linearly converting each attribute in the target attributes to obtain linear values;
Performing fast Fourier transform on the target attribute according to the linear value to obtain attribute signals, and extracting time domain features and frequency domain features of each signal in the attribute signals;
and carrying out feature combination on the time domain features and the frequency domain features to obtain target features, and taking the target features as feature attributes of the data attributes.
3. The method for integrally building data based on cloud computing as claimed in claim 2, wherein said feature combining the time domain features and the frequency domain features to obtain target features comprises:
respectively constructing feature matrixes of the time domain features and the frequency domain features to obtain a first feature matrix and a second feature matrix;
and carrying out weighted summation on the first feature matrix and the second feature matrix through the following formula to obtain a target feature matrix:
wherein B represents a target feature matrix, S represents a sigmoid function, a represents an initial matrix in the first feature matrix and the second feature matrix, z represents the total number of the first feature matrix and the second feature matrix, D represents the average value of each matrix in the first feature matrix and the second feature matrix, and D a Representing the average value of the a-th matrix, e (a, z) representing the range to which the matrix belongs;
And calculating a target feature value corresponding to the target feature matrix, and obtaining a target feature according to the target feature value.
4. The method for integrally building data based on cloud computing according to claim 1, wherein calculating the relevance of each data in the cloud computing data according to the characteristic attribute to obtain the data relevance comprises:
extracting a characteristic label of each data in the cloud computing data according to the characteristic attribute;
constructing a feature vector of each tag in the feature tags;
and calculating the relevance of each data in the cloud computing data according to the feature vector to obtain the data relevance.
5. The method for integrally building data based on cloud computing as claimed in claim 4, wherein said calculating the relevance of each data in the cloud computing data according to the feature vector, to obtain the data relevance, comprises:
the relevance of each data in the cloud computing data is calculated by the following formula:
wherein C represents the data relevance of each data in the cloud computing data, E represents the dimension coefficient of the cloud computing data, Y represents the data quantity value of the cloud computing data, j represents the starting value of the cloud computing data, F j Corresponding feature vector lnF representing jth cloud computing data j The j-th cloud calculates the logarithmic value of the eigenvector of the data, F j+1 Corresponding eigenvectors, lnF, representing the j+1th cloud computing data j+1 The logarithmic value of the feature vector representing the j+1th cloud computing data, max () represents the maximum value of the logarithmic difference value, min () represents the minimum value of the logarithmic difference value, and ω represents the correlation coefficient of the cloud computing data.
6. The method for integrally building data based on cloud computing as claimed in claim 1, wherein said calculating the complexity of each data in said merged data, resulting in a data complexity, comprises:
calculating the weight of each data in the combined data to obtain a data weight value, and carrying out data sequencing on the combined data according to the data weight value to obtain a data sequence;
performing numerical processing on each data in the merged data to obtain a data value, and constructing a scatter diagram corresponding to each data according to the data value and the data sequence;
calculating the complexity of each graph in the scatter graph to obtain image complexity, and determining the data complexity of the combined data according to the image complexity.
7. The method for integrally building data based on cloud computing as claimed in claim 6, wherein said calculating the complexity of each graph in said scatter plot, resulting in an image complexity, comprises:
the complexity of each plot in the scatter plot is calculated by the following formula:
where G represents the image complexity, H, of each of the scatter plots k Representing the length of a graphics bus corresponding to a kth scatter diagram in the scatter diagrams, M k Represents the coordinate mean value of the x-axis corresponding to the kth scatter diagram, N k Represents the coordinate mean value of the y-axis corresponding to the kth scattergram, and ∈cd represents the integral of the coordinate mean difference between the x-axis and the y-axis corresponding to the kth scattergram.
8. Device that data integration was built based on cloud calculates, its characterized in that, the device includes:
the feature extraction module is used for acquiring cloud computing data to be processed, carrying out attribute analysis on the cloud computing data to obtain data attributes, and carrying out feature extraction on the data attributes to obtain feature attributes;
the data merging module is used for calculating the relevance of each data in the cloud computing data according to the characteristic attribute to obtain data relevance, and carrying out data merging on the cloud computing data according to the data relevance to obtain merged data;
The node configuration module is used for identifying the data type corresponding to the combined data, configuring the data nodes of the combined data according to the data type, and calculating the complexity of each data in the combined data to obtain the data complexity;
and the platform construction module is used for setting the data level of each data in the merged data according to the data complexity and constructing an integrated platform of the merged data by combining the data nodes and the data level.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cloud computing based data integration construction method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for integrally building cloud computing-based data according to any one of claims 1 to 7.
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