CN116841989A - Lightweight enterprise data center system based on artificial intelligence and construction method - Google Patents
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Abstract
The embodiment of the application provides an artificial intelligence-based lightweight enterprise data center system and a construction method thereof, belonging to the technical field of data processing, wherein the method comprises the following steps: acquiring original data Q which is stored in a current data center and contains a plurality of objects, and dividing the original data into N independent data units through a classification function g (x); when new added data Q 'exists in the data center, determining the attribution value p of the new added data Q' in N independent data units; carrying out data analysis on the newly added data Q 'through a data analysis expression fp (x), and obtaining a data analysis expression fp (x)' and a data characteristic value tp 'of the newly added data Q'; when a query request for the data center exists, determining the data volume corresponding to the query request through the data characteristic value sequences { t1, … ti … tN } and the data characteristic values tp'. By adopting the scheme, the data center can be constructed in a light-weight mode, and the cost of the data center is reduced.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an artificial intelligence-based lightweight enterprise data center system and a construction method thereof.
Background
Manufacturers of big data services in China, for example: the data center of the Ali, the Kangaroo cloud, the bil platform of the bil technology and the traditional software manufacturer, like the Apuic of the golden butterfly, are all data center based on big data technology. However, these data center stations have these problems: 1. the traditional data is heavy: the cloud resource is heavy (7-15 servers above 8C are usually needed), the economic cost is high, the product cost is usually millions or even tens of millions, and the deployment and implementation period is long; 2. heavy base, light application. Most of the time for project implementation is in the base construction part, project results cannot be seen, and the implementation period is long, so that doubts are easily generated on the confidence and the target of the project. For these two main reasons, the support strength is insufficient for small and medium enterprises and government units of the first class of the ground city.
Disclosure of Invention
In view of the above, embodiments of the present application provide an artificial intelligence-based lightweight enterprise data center system and a construction method thereof, which at least partially solve the problems existing in the prior art.
In a first aspect, an embodiment of the present application provides a method for constructing a lightweight enterprise data center based on artificial intelligence, including:
acquiring original data Q which is stored in a current data center and contains a plurality of objects, and segmenting the original data into N independent data units through a classification function g (x) so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data characteristic value sequence { t1, … ti … tN } for the independent data units, wherein 1.ltoreq.i.ltoreq.N;
when new added data Q 'exists in a data center, after object classification information F of the new added data is obtained, matching calculation is carried out on the object classification information F and a data characteristic value ti by utilizing a matching function h (x), and the attribution value p of the new added data Q' in N independent data units is determined based on the result of max { h (F, ti) }, wherein p is more than or equal to 1 and less than or equal to N;
carrying out data analysis on the newly added data Q 'through a data analysis expression fp (x), and obtaining a data analysis expression fp (x)' and a data characteristic value tp 'of the newly added data Q';
when a query request for the data center exists, determining the data volume corresponding to the query request through the data eigenvalue sequence { t1, … ti … tN } and the data eigenvalue tp ', so as to select different search engines according to the size of the data volume to execute data query for the data analysis expression sequences { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'.
According to a specific implementation manner of the embodiment of the present disclosure, the splitting the raw data into N independent data units by the classification function g (x) includes:
acquiring the number L of objects contained in original data Q stored in a data center;
using classification functionsCalculating the similarity between the data contained in the L objects and the classification test sample containing the N classification labels, wherein u is a normalization coefficient, y is the classification test sample,the similarity between the ith object in the L objects and the test sample y is calculated;
based on the calculated similarity value, the data contained in the L objects are partitioned into N independent data units.
According to a specific implementation manner of the embodiment of the present disclosure, the generating the corresponding data parsing expression sequence { f1 (x), … fi (x) …, fn (x) } and the data eigenvalue sequence { t1, … ti … tN } for the independent data units includes:
data analysis is carried out on the data stored in the independent data unit, and the internal relation of the data stored in the independent data unit is obtained;
modeling and calculating the internal relation to obtain a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } generated for the independent data unit;
the data stored in the independent data unit is subjected to eigenvalue calculation by using a data analysis expression sequence { f1 (x), … fi (x) …, fn (x) }, and a data eigenvalue sequence { t1, … ti … tN }.
According to a specific implementation manner of the embodiment of the present disclosure, before the generating the corresponding data parsing expression sequence { f1 (x), … fi (x) …, fn (x) } and the data eigenvalue sequence { t1, … ti … tN } for the independent data units, the method further includes:
sequencing the data contained in the independent data units according to a preset sequence to obtain a data sequence;
performing a duplicate-checking operation on the data in the data sequence so as to remove duplicate data contained in the independent data unit;
and performing layering operation on the data after the duplicate checking, so that the size of the data storage space occupied by the independent data units is minimized.
According to a specific implementation manner of the embodiment of the present disclosure, the matching calculation for the object classification information F and the data feature value ti by using the matching function h (x) includes:
using functionsCalculating the maximum value of matching between the object classification information F and the data characteristic value;
based on the maximum value, the attribution value p of the newly added data Q' in N independent data units is determined.
According to a specific implementation manner of the embodiment of the present disclosure, the data analysis is performed on the newly added data Q 'by using the data analysis expression fp (x), to obtain a data analysis expression fp (x)' and a data characteristic value tp 'for the newly added data Q', including:
calculating the characteristic value of the newly added data Q 'through a data analysis expression fp (x) to obtain a data characteristic value tp';
calculating a difference Cp between the data characteristic value tp and the data characteristic value tp';
and taking the difference Cp as a coefficient of a newly added analysis item of the data analysis expression fp (x) to obtain a data analysis expression fp (x) ', of newly added data Q'.
According to a specific implementation manner of the embodiment of the present disclosure, the determining, by the data eigenvalue sequence { t1, … ti … tN } and the data eigenvalue tp', the data amount corresponding to the query request includes:
determining a data analysis expression fc (x) corresponding to the query request based on the data characteristic value sequences { t1, … ti … tN } and the data characteristic values tp';
and calculating the complexity value of the data analysis expression fc (x) so as to determine the data volume corresponding to the query request based on the complexity value. .
According to a specific implementation manner of the embodiment of the present disclosure, the selecting different search engines according to the size of the data volume to execute the data query for the data analysis expression sequence { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)' includes:
acquiring data processing capacities corresponding to a plurality of search engines preset in the data center;
and determining the search engine matched with the data size based on the data processing capacity corresponding to the search engine.
In a second aspect, embodiments of the present application provide an artificial intelligence based lightweight enterprise data center system, comprising:
the acquisition module is used for acquiring original data Q which is stored in the current data center and contains a plurality of objects, and dividing the original data into N independent data units through a classification function g (x) so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data characteristic value sequence { t1, … ti … tN } for the independent data units, wherein i is more than or equal to 1 and less than or equal to N;
the matching module is used for carrying out matching calculation on the object classification information F and the data characteristic value ti by utilizing a matching function h (x) after obtaining the object classification information F of the new data when the new data Q 'exists in the data center, and determining the attribution value p of the new data Q' in N independent data units based on the result of max { h (F, ti) }, wherein p is more than or equal to 1 and less than or equal to N;
the analysis module is used for carrying out data analysis on the newly added data Q 'through a data analysis expression fp (x) to obtain a data analysis expression fp (x)' and a data characteristic value tp 'of the newly added data Q';
and the determining module is used for determining the data quantity corresponding to the query request through the data characteristic value sequences { t1, … ti … tN } and the data characteristic value tp 'when the query request for the data center exists, so that different search engines can be selected according to the data quantity to execute data query for the data analysis expression sequences { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'. .
In a third aspect, an embodiment of the present application further provides an electronic device, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based lightweight enterprise data center method of any of the foregoing Ren Di aspects or first aspect implementations.
In a fourth aspect, embodiments of the present application also provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the artificial intelligence based lightweight enterprise data center method of the first aspect or any implementation of the first aspect.
In a fifth aspect, embodiments of the present application also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the artificial intelligence based lightweight enterprise data center method of the first aspect or any implementation of the first aspect.
The lightweight enterprise data center scheme based on artificial intelligence in the embodiment of the application comprises the following steps: acquiring original data Q which is stored in a current data center and contains a plurality of objects, and segmenting the original data into N independent data units through a classification function g (x) so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data characteristic value sequence { t1, … ti … tN } for the independent data units, wherein 1.ltoreq.i.ltoreq.N; when new added data Q 'exists in a data center, after object classification information F of the new added data is obtained, matching calculation is carried out on the object classification information F and a data characteristic value ti by utilizing a matching function h (x), and the attribution value p of the new added data Q' in N independent data units is determined based on the result of max { h (F, ti) }, wherein p is more than or equal to 1 and less than or equal to N; carrying out data analysis on the newly added data Q 'through a data analysis expression fp (x), and obtaining a data analysis expression fp (x)' and a data characteristic value tp 'of the newly added data Q'; when a query request for the data center exists, determining the data volume corresponding to the query request through the data eigenvalue sequence { t1, … ti … tN } and the data eigenvalue tp ', so as to select different search engines according to the size of the data volume to execute data query for the data analysis expression sequences { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'. According to the scheme provided by the application, the data center can be constructed in a light-weight mode, so that the cost of the data center is reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a lightweight enterprise data center construction method based on artificial intelligence according to an embodiment of the present application;
FIG. 2 is a schematic flow diagram of another lightweight enterprise data center construction method based on artificial intelligence according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of another method for constructing a lightweight enterprise data center based on artificial intelligence according to an embodiment of the present application;
FIG. 4 is a schematic flow diagram of another lightweight enterprise data center construction method based on artificial intelligence according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a lightweight enterprise data center system architecture based on artificial intelligence according to an embodiment of the present application;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present application.
Description of the embodiments
Embodiments of the present application will be 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 is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
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.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
Embodiments of the present disclosure provide a lightweight enterprise data center method based on artificial intelligence. The lightweight enterprise data center method based on artificial intelligence provided in this embodiment may be implemented by a computing device, which may be implemented as software or as a combination of software and hardware, and the computing device may be integrally provided in a server, a terminal device, or the like.
Referring to fig. 1, 2, 3 and 4, embodiments of the present disclosure provide an artificial intelligence based lightweight enterprise data center method comprising:
s101, acquiring original data Q which is stored in a current data center and contains a plurality of objects, and segmenting the original data into N independent data units through a classification function g (x), so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data characteristic value sequence { t1, … ti … tN } for the independent data units, wherein i is more than or equal to 1 and less than or equal to N.
In the continuous change process of the data stored in the data center, before no data is stored in the data center, each department statistical index is calculated according to own requirements, and data communication and communication are not carried out. If there is the same index requirement, the calculation is repeated twice. Resulting in wasted time and labor costs. The data in databases of different departments and different business information systems cannot be communicated, can be stored in the respective databases, cannot be uniformly utilized, and has no global view to the whole enterprise. In this way, the data of each department and each business system are mutually separated, and the data are like an island of a seat outside the sea, cannot be connected with each other and cannot be communicated, and are the data islands which are frequently heard at ordinary times. The data center is to form a common data layer in the whole business, eliminate decimal bins of the cross departments and realize multiplexing of data, so that the data is emphasized to be processed once, and the data of different departments cannot be repeatedly processed because of different application scenes.
For this purpose, the number L of objects (e.g., departments) contained in the original data Q stored in the data center can be obtained, using a classification functionCalculating the similarity between the data contained in the L objects and the classification test sample containing N classification labels, wherein u is a normalization coefficient, y is the classification test sample, < + >>For calculating the similarity between the ith object of the L objects and the test sample y. Based on the calculated similarity value, the data contained in the L objects are partitioned into N independent data units. The data in the independent data unit can be directly used for independent data query service without joint query with other objects, so that the pressure of data storage and data query in the data center can be relieved.
In order to further perform light weight processing on the data center, data analysis is performed on the data stored in the independent data unit, and the internal relation of the data stored in the independent data unit is obtained; modeling and calculating the internal relation to obtain a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } generated for the independent data unit; the data stored in the independent data unit is subjected to eigenvalue calculation by using a data analysis expression sequence { f1 (x), … fi (x) …, fn (x) }, and a data eigenvalue sequence { t1, … ti … tN }. The data analysis expression and the data characteristic value can express the data in a lighter mode, so that the storage and query pressure of the data center can be reduced.
S102, when new added data Q 'exists in a data center, after object classification information F of the new added data is obtained, matching calculation is carried out on the object classification information F and a data characteristic value ti by utilizing a matching function h (x), and the attribution value p of the new added data Q' in N independent data units is determined based on the result of max { h (F, ti) }, wherein p is more than or equal to 1 and less than or equal to N.
For the newly added data Q ', classification information F of the newly added data Q' may be calculated by a classification function, and then by using the functionThe maximum value of the match between the object classification information F and the data characteristic value is calculated. By calculating the maximum value, the most probable classification attribute of the newly added data Q 'can be calculated, so that the attribution value p of the newly added data Q' in the N independent data units is finally determined. The attribution value p is used for representing the data attribution relation between the newly added data Q' and the original N independent data units. By establishing the data attribution relation, the relation between the newly-added data and the original data can be established, and the newly-added data can be managed conveniently.
S103, data analysis is carried out on the newly added data Q 'through the data analysis expression fp (x), and the data analysis expression fp (x)' and the data characteristic value tp 'of the newly added data Q' are obtained.
The newly added data may be parsed in a variety of ways. As a way, the feature value calculation can be performed on the newly added data Q 'through a data analysis expression fp (x) to obtain a data feature value tp'; calculating a difference Cp between the data characteristic value tp and the data characteristic value tp'; and taking the difference Cp as a coefficient of a newly added analysis item of the data analysis expression fp (x) to obtain a data analysis expression fp (x) ', of newly added data Q'.
And S104, when a query request for the data center exists, determining the data quantity corresponding to the query request through the data characteristic value sequences { t1, … ti … tN } and the data characteristic value tp ', so as to select different search engines according to the data quantity to execute data query for the data analysis expression sequences { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'.
When a data query request exists in a data center, determining a data analysis expression fc (x) corresponding to the query request based on a data characteristic value sequence { t1, … ti … tN } and a data characteristic value tp'; and calculating the complexity value of the data analysis expression fc (x) so as to determine the data volume corresponding to the query request based on the complexity value.
For different search engines, a data center may employ a variety of different search engines to facilitate determining which search engine to select for searching of data based on the processing power of the different search engines. In this way, the data processing capacity corresponding to the search engines preset in the data center can be obtained; and determining the search engine matched with the data size based on the data processing capacity corresponding to the search engine.
For example, mySQL is used with a small amount of data; the possible use of small data size is HBase; the possible need for multidimensional analysis is greenplus; redis is needed for high real-time requirements.
Different access interfaces are customized for different query engines, applications are developed. The API interface shields the bottom data storage for application development, and uses the unified API interface to inquire data, thereby improving the speed of data access. On the other hand, for data development, the management efficiency of data application is improved, and the link relation from the table to the application is established.
Referring to fig. 2, according to a specific implementation manner of the embodiment of the present disclosure, the splitting the raw data into N independent data units by the classification function g (x) includes:
s201, acquiring the number L of objects contained in original data Q stored in a data center;
s202, using classification functionCalculating the similarity between the data contained in the L objects and the classification test sample containing the N classification labels, wherein u is a normalization coefficient, y is the classification test sample,for calculatingSimilarity between the ith object in the L objects and the test sample y;
s203, dividing the data contained in the L objects into N independent data units based on the calculated similarity values.
Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the generating the corresponding data parsing expression sequence { f1 (x), … fi (x) …, fn (x) } and the data eigenvalue sequence { t1, … ti … tN } for the independent data units includes:
s301, data analysis is carried out on the data stored in the independent data unit, and the internal relation of the data stored in the independent data unit is obtained;
s302, modeling and calculating the internal relation to obtain a data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } corresponding to the independent data unit;
s303, performing eigenvalue calculation on the data stored in the independent data unit by using the data analysis expression sequence { f1 (x), … fi (x) …, fn (x) }, and obtaining a data eigenvalue sequence { t1, … ti … tN }.
Referring to fig. 4, before the generating the corresponding data parsing expression sequence { f1 (x), … fi (x) …, fn (x) } and the data eigenvalue sequence { t1, … ti … tN } for the independent data units, according to a specific implementation of the embodiment of the present disclosure, the method further includes:
s401, sorting the data contained in the independent data units according to a preset sequence to obtain a data sequence;
s402, performing a duplicate checking operation on the data in the data sequence so as to remove duplicate data contained in the independent data unit;
s403, layering operation is carried out on the data after duplicate checking, so that the size of the data storage space occupied by the independent data unit is minimized.
In the layering operation process, the data after repeated searching can be split into multiple layers of data according to multiple attributes contained in the data after repeated searching, and the data of each layer has the same data attribute, so that the size of the data storage space occupied by the independent data unit is minimized.
According to a specific implementation manner of the embodiment of the present disclosure, the matching calculation for the object classification information F and the data feature value ti by using the matching function h (x) includes:
using functionsCalculating the maximum value of matching between the object classification information F and the data characteristic value;
based on the maximum value, the attribution value p of the newly added data Q' in N independent data units is determined.
According to a specific implementation manner of the embodiment of the present disclosure, the data analysis is performed on the newly added data Q 'by using the data analysis expression fp (x), to obtain a data analysis expression fp (x)' and a data characteristic value tp 'for the newly added data Q', including:
calculating the characteristic value of the newly added data Q 'through a data analysis expression fp (x) to obtain a data characteristic value tp';
calculating a difference Cp between the data characteristic value tp and the data characteristic value tp';
and taking the difference Cp as a coefficient of a newly added analysis item of the data analysis expression fp (x) to obtain a data analysis expression fp (x) ', of newly added data Q'.
According to a specific implementation manner of the embodiment of the present disclosure, the determining, by the data eigenvalue sequence { t1, … ti … tN } and the data eigenvalue tp', the data amount corresponding to the query request includes:
determining a data analysis expression fc (x) corresponding to the query request based on the data characteristic value sequences { t1, … ti … tN } and the data characteristic values tp';
and calculating the complexity value of the data analysis expression fc (x) so as to determine the data volume corresponding to the query request based on the complexity value. .
According to a specific implementation manner of the embodiment of the present disclosure, the selecting different search engines according to the size of the data volume to execute the data query for the data analysis expression sequence { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)' includes:
acquiring data processing capacities corresponding to a plurality of search engines preset in the data center;
and determining the search engine matched with the data size based on the data processing capacity corresponding to the search engine.
Referring to FIG. 5, an embodiment of the present application also discloses an artificial intelligence based lightweight enterprise data center system 50 comprising:
an obtaining module 501, configured to obtain original data Q containing a plurality of objects stored in a current data center, and segment the original data into N independent data units by using a classification function g (x), so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data eigenvalue sequence { t1, … ti … tN } for the independent data units, where 1.ltoreq.i.ltoreq.n;
the matching module 502 is configured to perform matching calculation on the object classification information F and the data feature value ti by using a matching function h (x) after obtaining the object classification information F of the new data when the new data Q 'exists in the data center, and determine the attribution value p of the new data Q' in N independent data units based on the result of max { h (F, ti) }, where p is equal to or greater than 1 and N;
the analysis module 503 is configured to perform data analysis on the newly added data Q 'by using a data analysis expression fp (x), so as to obtain a data analysis expression fp (x)' and a data characteristic value tp 'for the newly added data Q';
a determining module 504, configured to determine, when there is a query request for the data center, a data amount corresponding to the query request through the data eigenvalue sequence { t1, … ti … tN } and the data eigenvalue tp ', so as to select different search engines according to the size of the data amount to execute data queries for the data analysis expression sequence { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'.
Referring to fig. 6, an embodiment of the present application also provides an electronic device 60, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based lightweight enterprise data center method of the foregoing method embodiments.
Embodiments of the present application also provide a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the foregoing method embodiments.
Embodiments of the present application also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the artificial intelligence based lightweight enterprise data center method of the foregoing method embodiments.
The apparatus of fig. 6 may perform the method of the embodiment of fig. 1-4, and reference is made to the relevant description of the embodiment of fig. 1-4 for parts of this embodiment not described in detail. And will not be described in detail herein.
Referring now to fig. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, the electronic device 60 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 shows the electronic device 60 with various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by the node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present application should be included in the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. The utility model provides a lightweight enterprise data center construction method based on artificial intelligence, which is characterized by comprising the following steps:
acquiring original data Q which is stored in a current data center and contains a plurality of objects, and segmenting the original data into N independent data units through a classification function g (x) so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data characteristic value sequence { t1, … ti … tN } for the independent data units, wherein 1.ltoreq.i.ltoreq.N;
when new added data Q 'exists in a data center, after object classification information F of the new added data is obtained, matching calculation is carried out on the object classification information F and a data characteristic value ti by utilizing a matching function h (x), and the attribution value p of the new added data Q' in N independent data units is determined based on the result of max { h (F, ti) }, wherein p is more than or equal to 1 and less than or equal to N;
carrying out data analysis on the newly added data Q 'through a data analysis expression fp (x), and obtaining a data analysis expression fp (x)' and a data characteristic value tp 'of the newly added data Q';
when a query request for the data center exists, determining the data volume corresponding to the query request through the data eigenvalue sequence { t1, … ti … tN } and the data eigenvalue tp ', so as to select different search engines according to the size of the data volume to execute data query for the data analysis expression sequences { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'.
2. The method according to claim 1, wherein said slicing the raw data into N individual data units by a classification function g (x) comprises:
acquiring the number L of objects contained in original data Q stored in a data center;
using classification functionsCalculating the similarity between the data contained in the L objects and the classification test sample containing N classification labels, wherein u is a normalization coefficient, y is the classification test sample, < + >>The similarity between the ith object in the L objects and the test sample y is calculated;
based on the calculated similarity value, the data contained in the L objects are partitioned into N independent data units.
3. The method of claim 2, wherein generating the corresponding data parsing expression sequence { f1 (x), … fi (x) …, fn (x) } and the data eigenvalue sequence { t1, … ti … tN } for the independent data units comprises:
data analysis is carried out on the data stored in the independent data unit, and the internal relation of the data stored in the independent data unit is obtained;
modeling and calculating the internal relation to obtain a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } generated for the independent data unit;
the data stored in the independent data unit is subjected to eigenvalue calculation by using a data analysis expression sequence { f1 (x), … fi (x) …, fn (x) }, and a data eigenvalue sequence { t1, … ti … tN }.
4. A method according to claim 3, wherein before generating the corresponding data parsing expression sequence { f1 (x), … fi (x) …, fn (x) } and the data eigenvalue sequence { t1, … ti … tN } for the individual data units, the method further comprises:
sequencing the data contained in the independent data units according to a preset sequence to obtain a data sequence;
performing a duplicate-checking operation on the data in the data sequence so as to remove duplicate data contained in the independent data unit;
and performing layering operation on the data after the duplicate checking, so that the size of the data storage space occupied by the independent data units is minimized.
5. A method according to claim 3, wherein said matching calculation of said object classification information F and data feature values ti using a matching function h (x) comprises:
using functionsCalculating the maximum value of matching between the object classification information F and the data characteristic value;
based on the maximum value, the attribution value p of the newly added data Q' in N independent data units is determined.
6. The method according to claim 5, wherein the data analysis of the newly added data Q 'by the data analysis expression fp (x) to obtain the data analysis expression fp (x)' and the data characteristic value tp 'of the newly added data Q', includes:
calculating the characteristic value of the newly added data Q 'through a data analysis expression fp (x) to obtain a data characteristic value tp';
calculating a difference Cp between the data characteristic value tp and the data characteristic value tp';
and taking the difference Cp as a coefficient of a newly added analysis item of the data analysis expression fp (x) to obtain a data analysis expression fp (x) ', of newly added data Q'.
7. The method of claim 6, wherein determining the amount of data corresponding to the query request from the sequence of data eigenvalues { t1, … ti … tN } and the data eigenvalue tp', comprises:
determining a data analysis expression fc (x) corresponding to the query request based on the data characteristic value sequences { t1, … ti … tN } and the data characteristic values tp';
and calculating the complexity value of the data analysis expression fc (x) so as to determine the data volume corresponding to the query request based on the complexity value. .
8. The method of claim 7, wherein selecting different search engines to execute data queries for the sequence of data parse expressions { f1 (x), … fi (x) … fn (x) } and the data parse expression fp (x)' according to the size of the data volume comprises:
acquiring data processing capacities corresponding to a plurality of search engines preset in the data center;
and determining the search engine matched with the data size based on the data processing capacity corresponding to the search engine.
9. A lightweight enterprise data center system based on artificial intelligence, comprising:
the acquisition module is used for acquiring original data Q which is stored in the current data center and contains a plurality of objects, and dividing the original data into N independent data units through a classification function g (x) so as to generate a corresponding data analysis expression sequence { f1 (x), … fi (x) …, fn (x) } and a data characteristic value sequence { t1, … ti … tN } for the independent data units, wherein i is more than or equal to 1 and less than or equal to N;
the matching module is used for carrying out matching calculation on the object classification information F and the data characteristic value ti by utilizing a matching function h (x) after obtaining the object classification information F of the new data when the new data Q 'exists in the data center, and determining the attribution value p of the new data Q' in N independent data units based on the result of max { h (F, ti) }, wherein p is more than or equal to 1 and less than or equal to N;
the analysis module is used for carrying out data analysis on the newly added data Q 'through a data analysis expression fp (x) to obtain a data analysis expression fp (x)' and a data characteristic value tp 'of the newly added data Q';
and the determining module is used for determining the data quantity corresponding to the query request through the data characteristic value sequences { t1, … ti … tN } and the data characteristic value tp 'when the query request for the data center exists, so that different search engines can be selected according to the data quantity to execute data query for the data analysis expression sequences { f1 (x), … fi (x) … fn (x) } and the data analysis expression fp (x)'.
10. 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 memory stores instructions executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based lightweight enterprise data center method as claimed in any one of claims 1 to 8.
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