Lightweight enterprise data center system based on artificial intelligence and construction method

Abstract

This invention provides a lightweight enterprise data center system and construction method based on artificial intelligence, belonging to the field of data processing technology. The method includes: acquiring raw data Q containing multiple objects stored in the current data center; dividing the raw data into N independent data units using a classification function g(x); when new data Q' exists in the data center, determining the affiliation value p of the new data Q' among the N independent data units; parsing the new data Q' using a data parsing expression fp(x) to obtain the data parsing expression fp(x)' and data feature value tp' for the new data Q'; when a query request exists for the data center, determining the amount of data corresponding to the query request using the data feature value sequence {t1,…ti…tN} and the data feature value tp'. This solution enables the lightweight construction of a data center, reducing data center costs.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a lightweight enterprise data center system based on artificial intelligence and its construction method. Background Technology

[0002] Domestic vendors engaged in big data business, such as Alibaba's data platform, Kangaroo Cloud's data stack, and Shulan Technology's data platform, as well as traditional software vendors like Kingdee's Apusic, all develop data platforms based on big data technology. However, these data platforms suffer from several problems: 1. Traditional data platforms are resource-intensive: requiring significant cloud resources (typically 7-15 servers with 8C or higher CPUs), resulting in high costs, often reaching millions or even tens of millions of yuan, and long deployment and implementation cycles; 2. They prioritize infrastructure over applications. Much of the project implementation time is spent on infrastructure construction, with no visible results. Coupled with the long implementation cycle, this can easily lead to doubts about the project's confidence and objectives. Due to these two main reasons, they offer insufficient support for SMEs and municipal-level government units. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a lightweight enterprise data center system and construction method based on artificial intelligence, which at least partially solves the problems existing in the prior art.

[0004] In a first aspect, embodiments of the present invention provide a method for constructing a lightweight enterprise data center based on artificial intelligence, comprising:

[0005] Obtain the original data Q containing multiple objects stored in the current data center, and divide the original data into N independent data units using the classification function g(x), so as to generate the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and data feature value sequence {t1,…ti…tN} for the independent data units, where 1≤i≤N;

[0006] When new data Q' is added to the data center, after obtaining the object classification information F of the new data, the matching function h(x) is used to perform matching calculation on the object classification information F and the data feature value ti, and based on the result of max{h(F, ti)}, the belonging value p of the new data Q' in N independent data units is determined, where 1≤p≤N;

[0007] The newly added data Q' is parsed using the data parsing expression fp(x) to obtain the data parsing expression fp(x)' and the data feature value tp' for the newly added data Q'.

[0008] When a query request for the data center exists, the amount of data corresponding to the query request is determined by the data feature value sequence {t1,…ti…tN} and the data feature value tp', so that different search engines can be selected to perform data queries on the data parsing expression sequence {f1(x), …fi(x)… fn(x)} and the data parsing expression fp(x)' based on the size of the data amount.

[0009] According to a specific implementation of this disclosure, the step of dividing the original data into N independent data units using a classification function g(x) includes:

[0010] Obtain the number L of objects contained in the raw data Q stored in the data center;

[0011] Using classification function Calculate the similarity between the data contained in L objects and a classification test sample containing N classification labels, where u is the normalization coefficient and y is the classification test sample. Used to calculate the similarity between the i-th object among L objects and the test sample y;

[0012] Based on the calculated similarity values, the data contained in the L objects are divided into N independent data units.

[0013] According to a specific implementation of this disclosure, the step of generating a corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and a data feature value sequence {t1,…ti…tN} for independent data units includes:

[0014] Data parsing is performed on the data stored in the independent data units to obtain the inherent relationships between the data stored in the independent data units;

[0015] Modeling and calculating the intrinsic relationships yields a sequence of data parsing expressions {f1(x), …fi(x)…, fn(x)} for each independent data unit;

[0016] The eigenvalues ​​of the data stored in the independent data units are calculated using the data parsing expression sequence {f1(x), …fi(x)…, fn(x)}, resulting in the data eigenvalue sequence {t1,…ti…tN}.

[0017] According to a specific implementation of this disclosure, before generating the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and the data feature value sequence {t1,…ti…tN} for each independent data unit, the method further includes:

[0018] The data contained in the independent data units are sorted according to a preset order to obtain a data sequence;

[0019] Perform a deduplication operation on the data in the data sequence to remove duplicate data contained in independent data units;

[0020] After deduplication, the data is layered to minimize the data storage space occupied by each independent data unit.

[0021] According to a specific implementation of this disclosure, the step of performing matching calculations on the object classification information F and data feature values ​​ti using the matching function h(x) includes:

[0022] Using functions Calculate the maximum value of the match between the object classification information F and the data feature values;

[0023] Based on the maximum value, determine the affiliation value p of the newly added data Q' in N independent data units.

[0024] According to a specific implementation of this disclosure, the step of parsing the newly added data Q' using the data parsing expression fp(x) to obtain the data parsing expression fp(x)' for the newly added data Q' and the data feature value tp' includes:

[0025] The eigenvalues ​​of the newly added data Q' are calculated using the data parsing expression fp(x) to obtain the data eigenvalues ​​tp'.

[0026] Calculate the difference Cp between the data feature value tp and the data feature value tp';

[0027] The difference Cp is used as the coefficient of the new analytical term in the data analytical expression fp(x) to obtain the data analytical expression fp(x)' of the new data Q'.

[0028] According to a specific implementation of this disclosure, determining the amount of data corresponding to the query request using the data feature value sequence {t1,…ti…tN} and the data feature value tp' includes:

[0029] Based on the data feature value sequence {t1,…ti…tN} and the data feature value tp', determine the data parsing expression fc(x) corresponding to the query request;

[0030] Calculate the complexity value of the data parsing expression fc(x) so as to determine the amount of data corresponding to the query request based on the complexity value.

[0031] According to a specific implementation of this disclosure, the step of selecting different search engines to perform data queries on the data parsing expression sequence {f1(x), …fi(x)…fn(x)} and the data parsing expression fp(x)' based on the size of the data volume includes:

[0032] Obtain the data processing capabilities corresponding to multiple search engines pre-configured in the data center;

[0033] Based on the data processing capabilities of the search engine, a search engine that matches the size of the data volume is determined.

[0034] Secondly, embodiments of the present invention provide a lightweight enterprise data center system based on artificial intelligence, comprising:

[0035] The acquisition module is used to acquire the original data Q containing multiple objects stored in the current data center, and divide the original data into N independent data units by the classification function g(x), so as to generate the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and data feature value sequence {t1,…ti…tN} for the independent data units, where 1≤i≤N;

[0036] The matching module is used to, when there is new data Q' in the data center, after obtaining the object classification information F of the new data, use the matching function h(x) to perform matching calculation on the object classification information F and the data feature value ti, and determine the belonging value p of the new data Q' in N independent data units based on the result of max{h(F, ti)}, where 1≤p≤N;

[0037] The parsing module is used to parse the newly added data Q' using the data parsing expression fp(x) to obtain the data parsing expression fp(x)' and the data feature value tp' for the newly added data Q'.

[0038] The determination module is used to, when a query request for the data center exists, determine the amount of data corresponding to the query request using the data feature value sequence {t1,…ti…tN} and the data feature value tp', so as to select different search engines to perform data queries on the data parsing expression sequence {f1(x), …fi(x)…fn(x)} and the data parsing expression fp(x)' based on the size of the data volume.

[0039] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0040] At least one processor; and,

[0041] The memory is communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the AI-based lightweight enterprise data center method in any of the first aspects or any implementations thereof.

[0043] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the AI-based lightweight enterprise data center method in the first aspect or any implementation thereof.

[0044] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computing program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the artificial intelligence-based lightweight enterprise data center method in the first aspect or any implementation thereof.

[0045] The lightweight enterprise data center solution based on artificial intelligence in this embodiment of the invention includes: acquiring raw data Q containing multiple objects stored in the current data center; dividing the raw data into N independent data units using a classification function g(x), so as to generate a corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and a data feature value sequence {t1,…ti…tN} for each independent data unit, where 1≤i≤N; when new data Q' is added to the data center, after obtaining the object classification information F of the new data, matching calculation is performed on the object classification information F and the data feature value ti using a matching function h(x), and based on max{h(F, The result of ti)} determines the affiliation value p of the newly added data Q' in N independent data units, where 1≤p≤N; the newly added data Q' is parsed using the data parsing expression fp(x)' to obtain the data parsing expression fp(x)' and data feature value tp' for the newly added data Q'; when there is a query request for the data center, the amount of data corresponding to the query request is determined by the data feature value sequence {t1,…ti…tN} and the data feature value tp', so that different search engines can be selected to perform data queries on the data parsing expression sequence {f1(x), …fi(x)…fn(x)} and the data parsing expression fp(x)' according to the size of the data volume. Through the solution of this application, a lightweight approach to building a data center can be adopted, reducing the cost of the data center. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A schematic diagram illustrating a lightweight enterprise data center construction method based on artificial intelligence, provided for an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of another lightweight enterprise data center construction method based on artificial intelligence provided in an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of another lightweight enterprise data center construction method based on artificial intelligence provided in an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of another lightweight enterprise data center construction method based on artificial intelligence provided in an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of a lightweight enterprise data center system architecture based on artificial intelligence provided in an embodiment of the present invention;

[0052] Figure 6 A schematic diagram of an electronic device provided in an embodiment of the present invention. Implementation

[0053] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0054] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0055] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0056] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0057] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0058] This disclosure provides a lightweight enterprise data center method based on artificial intelligence. The lightweight enterprise data center method based on artificial intelligence provided in this embodiment can be executed by a computing device, which can be implemented as software or as a combination of software and hardware, and can be integrated into servers, terminal devices, etc.

[0059] See Figure 1 , Figure 2 , Figure 3 and Figure 4 This disclosure provides a lightweight enterprise data center method based on artificial intelligence, including:

[0060] S101, Obtain the original data Q containing multiple objects stored in the current data center, and divide the original data into N independent data units through the classification function g(x), so as to generate the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and data feature value sequence {t1,…ti…tN} for the independent data units, where 1≤i≤N.

[0061] Before the advent of a data platform, data stored in data centers is constantly changing. Each department calculates its own statistical indicators according to its own needs, without communicating or exchanging data. If the same indicator is required, it will be calculated twice, resulting in wasted time and manpower. Furthermore, data from different departments and business information systems is often incompatible, stored separately and unable to be used uniformly, lacking a holistic perspective for the entire enterprise. This creates data silos, like isolated islands, preventing communication and collaboration. A data platform aims to create a shared data layer across the entire business, eliminating these cross-departmental data warehouses and enabling data reuse. Therefore, it emphasizes processing data only once, avoiding duplicate processing for different application scenarios or departments.

[0062] To this end, the number L of objects (e.g., departments) contained in the raw data Q stored in the data center can be obtained using a classification function. Calculate the similarity between the data contained in L objects and a classification test sample containing N classification labels, where u is the normalization coefficient and y is the classification test sample. This is used to calculate the similarity between the i-th object among L objects and the test sample y. Based on the calculated similarity value, the data contained in the L objects are divided into N independent data units. The data in the independent data units can be directly used for independent data query services without needing to perform joint queries with other objects, thereby reducing the pressure on data storage and data querying in the data center.

[0063] To further streamline data center processing, data parsing is performed on the data stored in individual data units to obtain the inherent relationships between the data within each unit. These relationships are then modeled and calculated to generate a corresponding sequence of data parsing expressions {f1(x), …fi(x)…, fn(x)} for each individual data unit. Using this sequence, feature values ​​are calculated on the data stored in each unit to obtain a sequence of data feature values ​​{t1,…ti…tN}. This lightweight approach to data parsing and feature values ​​reduces the storage and query load on the data center.

[0064] S102, when there is new data Q' in the data center, after obtaining the object classification information F of the new data, the matching function h(x) is used to perform matching calculation on the object classification information F and the data feature value ti, and based on the result of max{h(F,ti)}, the belonging value p of the new data Q' in N independent data units is determined, where 1≤p≤N.

[0065] For newly added data Q', the classification information F of the newly added data Q' can be calculated using a classification function, and then the function can be used to... The maximum value found between the classification information F of the calculated object and the data feature values ​​is used to determine the most likely classification attribute of the new data Q', thus ultimately determining the attribution value p of the new data Q' among the N independent data units. The attribution value p characterizes the data attribution relationship between the new data Q' and the existing N independent data units. Establishing this data attribution relationship creates a link between the new data and the existing data, facilitating the management of the new data.

[0066] S103, perform data parsing on the newly added data Q' using the data parsing expression fp(x) to obtain the data parsing expression fp(x)' and the data feature value tp' for the newly added data Q'.

[0067] There are several ways to parse the new data. One way is to calculate the feature value of the new data Q' using the data parsing expression fp(x) to obtain the data feature value tp'; calculate the difference Cp between the data feature value tp and the data feature value tp'; and use the difference Cp as the coefficient of the new parsing term in the data parsing expression fp(x) to obtain the data parsing expression fp(x)' for the new data Q'.

[0068] S104, when there is a query request for the data center, the amount of data corresponding to the query request is determined by the data feature value sequence {t1,…ti…tN} and the data feature value tp', so as to select different search engines to perform data queries on the data parsing expression sequence {f1(x), …fi(x)…fn(x)} and the data parsing expression fp(x)' according to the size of the data amount.

[0069] When a data query request exists in the data center, the data parsing expression fc(x) corresponding to the query request can be determined based on the data feature value sequence {t1,…ti…tN} and the data feature value tp'; the complexity value of the data parsing expression fc(x) is calculated so that the amount of data corresponding to the query request can be determined based on the complexity value.

[0070] Data centers can employ multiple search engines to determine which one to use for data retrieval based on the processing capabilities of each. This allows for the acquisition of the data processing capabilities of multiple pre-configured search engines within the data center; and, based on these capabilities, a search engine matching the data volume can be selected.

[0071] For example, MySQL is used for small datasets; HBase may be used for large datasets; Greenplum may be needed for multidimensional analysis; and Redis is needed for high real-time requirements.

[0072] For different query engines, application developers customize different access interfaces. On the one hand, the API interface shields application development from the underlying data storage, allowing for data queries using a unified standard API, thus improving data access speed. On the other hand, for data developers, it improves the management efficiency of data applications and establishes a link between tables and applications.

[0073] See Figure 2 According to a specific implementation of this disclosure, dividing the original data into N independent data units using a classification function g(x) includes:

[0074] S201, Obtain the number L of objects contained in the raw data Q stored in the data center;

[0075] S202, using a classification function Calculate the similarity between the data contained in L objects and a classification test sample containing N classification labels, where u is the normalization coefficient and y is the classification test sample. Used to calculate the similarity between the i-th object among L objects and the test sample y;

[0076] S203, based on the calculated similarity value, divide the data contained in the L objects into N independent data units.

[0077] See Figure 3 According to a specific implementation of this disclosure, the step of generating a corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and a data feature value sequence {t1,…ti…tN} for independent data units includes:

[0078] S301, perform data parsing on the data stored in the independent data unit to obtain the inherent relationship between the data stored in the independent data unit;

[0079] S302, Model and calculate the intrinsic relationship to obtain the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} for each independent data unit;

[0080] S303, using the data parsing expression sequence {f1(x), …fi(x)…, fn(x)}, the eigenvalues ​​of the data stored in the independent data units are calculated to obtain the data eigenvalue sequence {t1,…ti…tN}.

[0081] See Figure 4 According to a specific implementation of this disclosure, before generating the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and the data feature value sequence {t1,…ti…tN} for independent data units, the method further includes:

[0082] S401, Sort the data contained in the independent data unit according to the preset order to obtain the data sequence;

[0083] S402, Perform a deduplication operation on the data in the data sequence to remove duplicate data contained in independent data units;

[0084] S403 performs a layering operation on the data after deduplication, minimizing the data storage space occupied by independent data units.

[0085] During the layered operation, the deduplicated data can be split into multiple layers based on the multiple attributes contained in the data after deduplication. Each layer of data has the same data attributes, thereby minimizing the data storage space occupied by independent data units.

[0086] According to a specific implementation of this disclosure, the step of performing matching calculations on the object classification information F and data feature values ​​ti using the matching function h(x) includes:

[0087] Using functions Calculate the maximum value of the match between the object classification information F and the data feature values;

[0088] Based on the maximum value, determine the affiliation value p of the newly added data Q' in N independent data units.

[0089] According to a specific implementation of this disclosure, the step of parsing the newly added data Q' using the data parsing expression fp(x) to obtain the data parsing expression fp(x)' for the newly added data Q' and the data feature value tp' includes:

[0090] The eigenvalues ​​of the newly added data Q' are calculated using the data parsing expression fp(x) to obtain the data eigenvalues ​​tp'.

[0091] Calculate the difference Cp between the data feature value tp and the data feature value tp';

[0092] The difference Cp is used as the coefficient of the new analytical term in the data analytical expression fp(x) to obtain the data analytical expression fp(x)' of the new data Q'.

[0093] According to a specific implementation of this disclosure, determining the amount of data corresponding to the query request using the data feature value sequence {t1,…ti…tN} and the data feature value tp' includes:

[0094] Based on the data feature value sequence {t1,…ti…tN} and the data feature value tp', determine the data parsing expression fc(x) corresponding to the query request;

[0095] Calculate the complexity value of the data parsing expression fc(x) so as to determine the amount of data corresponding to the query request based on the complexity value.

[0096] According to a specific implementation of this disclosure, the step of selecting different search engines to perform data queries on the data parsing expression sequence {f1(x), …fi(x)…fn(x)} and the data parsing expression fp(x)' based on the size of the data volume includes:

[0097] Obtain the data processing capabilities corresponding to multiple search engines pre-configured in the data center;

[0098] Based on the data processing capabilities of the search engine, a search engine that matches the size of the data volume is determined.

[0099] See Figure 5 This invention also discloses a lightweight enterprise data center system 50 based on artificial intelligence, comprising:

[0100] The acquisition module 501 is used to acquire the original data Q containing multiple objects stored in the current data center, and divide the original data into N independent data units by the classification function g(x), so as to generate the corresponding data parsing expression sequence {f1(x), …fi(x)…, fn(x)} and data feature value sequence {t1,…ti…tN} for the independent data units, where 1≤i≤N;

[0101] The matching module 502 is used to, when there is new data Q' in the data center, after obtaining the object classification information F of the new data, use the matching function h(x) to perform matching calculation on the object classification information F and the data feature value ti, and determine the belonging value p of the new data Q' in N independent data units based on the result of max{h(F, ti)}, where 1≤p≤N;

[0102] The parsing module 503 is used to parse the newly added data Q' using the data parsing expression fp(x) to obtain the data parsing expression fp(x)' and the data feature value tp' for the newly added data Q'.

[0103] The determination module 504 is used to determine the amount of data corresponding to the query request when there is a query request for the data center, through the data feature value sequence {t1,…ti…tN} and the data feature value tp', so as to select different search engines to perform data queries on the data parsing expression sequence {f1(x), …fi(x)…fn(x)} and the data parsing expression fp(x)' according to the size of the data volume.

[0104] See Figure 6 This invention also provides an electronic device 60, which includes:

[0105] At least one processor; and,

[0106] The memory is communicatively connected to the at least one processor; wherein,

[0107] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the AI-based lightweight enterprise data center method described in the foregoing method embodiments.

[0108] This invention also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform the aforementioned method embodiments.

[0109] This invention also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the lightweight enterprise data center method based on artificial intelligence in the foregoing method embodiments.

[0110] Figure 6 The device shown can perform Figure 1-4 For the methods shown in the embodiments, the parts not described in detail in this embodiment can be referred to the following: Figure 1-4The relevant descriptions of the illustrated embodiments are not repeated here.

[0111] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device 60 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0112] like Figure 6 As shown, electronic device 60 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of electronic device 60. Processing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0113] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 60 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 60 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0114] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of embodiments of this disclosure.

[0115] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0116] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0117] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire at least two Internet Protocol (IP) addresses; send a node evaluation request including the at least two IP addresses to a node evaluation device, wherein the node evaluation device selects an IP address from the at least two IP addresses and returns it; and receive the IP address returned by the node evaluation device; wherein the acquired IP address indicates an edge node in a content delivery network.

[0118] Alternatively, the aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receive a node evaluation request including at least two Internet Protocol (IP) addresses; select an IP address from the at least two IP addresses; and return the selected IP address; wherein the received IP address indicates an edge node in the content delivery network.

[0119] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0121] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0122] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof.

[0123] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An artificial intelligence-based lightweight enterprise data center construction method, characterized by, The method comprises the following steps: Obtaining original data Q containing a plurality of objects stored in a current data center, and dividing the original data into N independent data units by 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≤i≤N; When there is new data Q' in the data center, after obtaining the object classification information F of the new data, the object classification information F and the data characteristic value ti are matched and calculated by a matching function h(x), and based on the result of max{h(F, ti)}, the attribution value p of the new data Q' in the N independent data units is determined, wherein 1≤p≤N; The data analysis expression fp(x)' and the data characteristic value tp' of the new data Q' are obtained by data analysis of the new data Q' through the data analysis expression fp(x). When there is a query request for the data center, the data volume corresponding to the query request is determined through the data characteristic value sequence {t1,…ti…tN} and the data characteristic value tp', so as to select different search engines to perform data query on the data analysis expression sequence {f1(x), …fi(x)… fn(x)} and the data analysis expression fp(x)' according to the size of the data volume, comprising: Determine the data analysis expression fc(x) corresponding to the query request based on the data characteristic value sequence {t1,…ti…tN} and the data characteristic value tp'; Calculate 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; Obtain the data processing capacity of a plurality of search engines pre-set in the data center; Determine the search engine matched with the size of the data volume based on the data processing capacity of the search engine.

2. The method of claim 1, wherein, The original data is divided into N independent data units by the classification function g(x), comprising: Obtaining the number L of objects contained in the original data Q stored in the data center; Using a classification function computing a similarity between data contained in the L objects and a classification test sample containing N classification labels, where u is a normalization coefficient, y is the classification test sample, for computing a similarity between the i-th object of the L objects and the test sample y; Based on the calculated similarity value, the data contained in the L objects is divided into N independent data units.

3. The method of claim 2, wherein, The corresponding data analysis expression sequence {f1(x), …fi(x)…, fn(x)} and the data characteristic value sequence {t1,…ti…tN} for the independent data units are generated, comprising: Data analysis is performed on the data stored in the independent data units to obtain the internal relationship of the data stored in the independent data units; The internal relationship is modeled and calculated to obtain the corresponding data analysis expression sequence {f1(x), …fi(x)…, fn(x)} for the independent data units; The data characteristic value sequence {t1,…ti…tN} is obtained by performing characteristic value calculation on the data stored in the independent data units by using the data analysis expression sequence {f1(x), …fi(x)…, fn(x)}.

4. The method of claim 3, wherein, Before the generating corresponding data analysis expression sequence {f1(x), …fi(x)…, fn(x)} and data characteristic value sequence {t1,…ti…tN} for the independent data unit, the method further comprises: sequencing the data contained in the independent data unit in a preset order to obtain a data sequence; performing a duplicate checking operation on the data in the data sequence so as to eliminate the duplicate data contained in the independent data unit; performing a layering operation on the data after the duplicate checking so that the size of the data storage space occupied by the independent data unit is minimized.

5. The method of claim 3, wherein, The matching calculation of the object classification information F and the data characteristic value ti by using the matching function h(x) comprises: Utilizing functions calculating a maximum value of matching between the object classification information F and the data characteristic value; Based on the maximum value, determining the belonging value p of the new data Q' in the N independent data units.

6. The method of claim 5, wherein, The data analysis of the new data Q' by using the data analysis expression fp(x) comprises: Characteristic value calculation of the new data Q' by using the data analysis expression fp(x) to obtain the data characteristic value tp'; Calculating the difference Cp between the data characteristic value tp and the data characteristic value tp'; Taking the difference Cp as the coefficient of the new analysis item of the data analysis expression fp(x) to obtain the data analysis expression fp(x)' of the new data Q'.

7. An artificial intelligence based lightweight enterprise data center system, characterized by, Comprise: The acquisition module is used to acquire the original data Q containing a plurality of objects stored in the current data center, and the original data is divided into N independent data units by using the classification function g(x) so as to generate corresponding data analysis expression sequence {f1(x), …fi(x)…, fn(x)} and data characteristic value sequence {t1,…ti…tN} for the independent data unit, wherein 1≤i≤N; The matching module is used to, when there is new data Q' in the data center, after obtaining the object classification information F of the new data, perform matching calculation of the object classification information F and the data characteristic value ti by using the matching function h(x), and based on the result of max{h(F, ti)}, determine the belonging value p of the new data Q' in the N independent data units, wherein 1≤p≤N; The analysis module is used to perform data analysis of the new data Q' by using the data analysis expression fp(x) to obtain the data analysis expression fp(x)' of the new data Q' and the data characteristic value tp'; The determination module is used to, when there is a query request for the data center, determine the data amount corresponding to the query request by using the data characteristic value sequence {t1,…ti…tN} and the data characteristic value tp', so as to select different search engines to perform data query for the data analysis expression sequence {f1(x), …fi(x)… fn(x)} and the data analysis expression fp(x)' according to the size of the data amount.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores instructions executable by the at least one processor, the instructions being executed 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 preceding claim 1-6.

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