Information processing method, apparatus, device, storage medium, and computer program product

Abstract

The application discloses an information processing method, device, equipment, storage medium and computer program product. The method comprises the following steps: obtaining first data, wherein the first data comprises data element information of a data center, a data center quantity, a data center energy consumption level and a first association relationship between the data center and an organization, and the first data is data obtained based on infrastructure; calculating the first data to obtain a label frequency of the data center and a label frequency of the organization; and generating a label based on the label frequency of the data center and the label frequency of the organization, wherein the label comprises the first association relationship, an organization label information table and a data center label information table, the label frequency of the organization is included in the organization label information table, and the label frequency of the data center is included in the data center label information table. According to the embodiment of the application, the label which can accurately describe the attribute of a user can be made for the data related to infrastructure.

Description

Technical Field

[0001] The present application relates to the field of big data and data analysis, and in particular to an information processing method, apparatus, device, storage medium, and computer program product. Background Art

[0002] With the rapid development of the internet and mobile internet, tags have been widely used in scenarios such as precision business operations, personalized recommendations, and intelligent delivery. Building a user tag system based on user attributes can analyze and abstract a user's complete information, enabling user profiling.

[0003] However, currently, most approaches rely on tagging dynamic data, such as user behavior, to conduct user profiling. Because infrastructure-related data is largely static, tagging systems based on this data struggle to accurately capture user attributes. Consequently, few tags exist specifically for infrastructure-related data. Summary of the Invention

[0004] The embodiments of the present application provide an information processing method, apparatus, device, and computer storage medium, which can generate labels that can accurately describe user attributes for infrastructure-related data.

[0005] In a first aspect, an embodiment of the present application provides an information processing method, the method comprising:

[0006] Acquire first data, where the first data includes data metadata of a data center, the number of data centers, an energy consumption level of a data center, and a first association relationship between a data center and an organization, and the first data is data acquired based on infrastructure data;

[0007] Calculating the first data to obtain the label frequency of the data center and the label frequency of the organization;

[0008] Based on the label frequency of the data center and the label frequency of the organization, a label is generated. The label includes a first association relationship, an organization label information table, and a data center label information table. The organization label information table includes the label frequency of the organization, and the data center label information table includes the label frequency of the data center.

[0009] In some embodiments of the first aspect, calculating the first data to obtain the label frequency of the data center and the label frequency of the institution includes:

[0010] Determine, based on the data metadata of the data center, the number of data centers, the energy consumption level of the data center, and the first association, the original label frequency of the data center, the data metadata of the organization, and the number of the organization;

[0011] The data element information of the data center, the number of data centers and the energy consumption level of the data center are calculated to obtain the energy consumption level data element weight of the data center;

[0012] The data element information of the institution and the number of institutions are calculated to obtain the data element uniform distribution weight;

[0013] The original label frequency of the data center is weighted according to the energy consumption level data element weight and the data element uniform distribution weight to obtain the label frequency of the data center;

[0014] The label frequency of the data center is processed according to the first association relationship to obtain the label frequency of the institution.

[0015] In some embodiments of the first aspect, the data center includes a plurality of data centers of different energy consumption levels, and the data element information of the data center, the number of data centers and the energy consumption level of the data center are calculated to obtain the energy consumption level data element weight of the data center, including:

[0016] According to the data element information of the data center and the number of data centers, the average number of each type of data element of each energy consumption level is calculated;

[0017] According to the average number of each type of data element of each energy consumption level, the energy consumption level data element weight of the data center is determined.

[0018] In some embodiments of the first aspect, the data element information of the institution and the number of institutions are calculated to obtain the data element uniform distribution weight, including:

[0019] According to the data element information of the institution and the number of institutions, the average number of each type of data element in the institution is calculated;

[0020] According to the average number of each type of data element in the institution, the data element uniform distribution weight is determined.

[0021] In some embodiments of the first aspect, before the first data is obtained, the method further includes:

[0022] Obtaining data of the infrastructure, the data of the infrastructure including data element information of the infrastructure, a second association relationship between the infrastructure and the data center and the first association relationship;

[0023] According to the data element information of the infrastructure and the second association relationship, the data element information of the data center, the number of data centers and the energy consumption level of the data center are determined.

[0024] In some embodiments of the first aspect, before generating the tag based on the tag frequency of the data center and the tag frequency of the organization, the method further includes: obtaining a tag information table of the infrastructure, the tag information table of the infrastructure including an identity identifier, a tag identifier, and a tag value of the infrastructure;

[0025] Obtaining identification information of the data center, where the data center representation information includes the identity of the data center and identification label information of the data center;

[0026] Obtain the organization's identification information, which includes the organization's identity and identification label information.

[0027] In some embodiments of the first aspect, the tag further includes a tag information table of the infrastructure and a second association relationship;

[0028] The institution label information table also includes the institution's identification information;

[0029] The data center label information table also includes identification information of the data center.

[0030] In some embodiments of the first aspect, the method further comprises:

[0031] Periodically obtain infrastructure data;

[0032] Update labels based on infrastructure data.

[0033] In some embodiments of the first aspect, after generating the tag based on the tag frequency of the data center and the tag frequency of the organization, the method further includes:

[0034] Based on the tags, a word cloud analysis is performed on at least one of the data center and / or the organization to generate a word cloud map of the data center and / or a word cloud map of the organization.

[0035] In some embodiments of the first aspect, after generating the tag based on the tag frequency of the data center and the tag frequency of the organization, the method further includes:

[0036] Based on tags, use business intelligence (BI) analysis tools to create a profile of at least one of the organization, data center, and infrastructure.

[0037] In some embodiments of the first aspect, after generating the tag based on the tag frequency of the data center and the tag frequency of the organization, the method further includes:

[0038] At least one of an infrastructure, a data center, and an organization is classified according to the label.

[0039] In a second aspect, an embodiment of the present application provides an information processing device, the device comprising:

[0040] A first acquisition module is configured to acquire first data, the first data including data metadata of a data center, the number of data centers, the energy consumption level of a data center, and a first association between a data center and an organization, the first data being data acquired based on infrastructure data;

[0041] A calculation module, configured to calculate the first data to obtain the label frequency of the data center and the label frequency of the organization;

[0042] The first generation module is used to generate labels based on the label frequency of the data center and the label frequency of the organization. The labels include a first association relationship, an organization label information table, and a data center label information table. The organization label information table includes the label frequency of the organization, and the data center label information table includes the label frequency of the data center.

[0043] In a third aspect, an embodiment of the present application provides an information processing device, the device comprising: a processor and a memory storing computer program instructions;

[0044] When the processor executes the computer program instructions, it implements the information processing method of any embodiment of the first aspect of the present application.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, an information processing method as in any embodiment of the first aspect of the present application is implemented.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes an information processing method as described in any embodiment of the first aspect of the present application.

[0047] The information processing methods, devices, equipment, computer storage media, and computer program products of the embodiments of the present application can calculate the label frequency of the data center and the label frequency of the organization based on the data metadata information of the data center obtained based on the infrastructure data, the energy consumption level of the data center, and the relationship between the data center and the organization. In this way, static infrastructure data can be converted into the label frequency of the data center and the organization. The labels generated based on the label frequency of the data center and the organization can be applied to the portrait analysis of the data center and the organization, thereby depicting the infrastructure development status of the data center and the organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 This is a flowchart of an information processing method provided by an embodiment of the present application;

[0050] Figure 2 is a flowchart of an information processing method provided by another embodiment of the present application;

[0051] Figure 3 This is a schematic diagram of a label structure provided by another embodiment of the present application;

[0052] Figure 4 is a structural diagram of an information processing device provided in yet another embodiment of the present application;

[0053] Figure 5 It is a structural diagram of an information processing device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0054] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0055] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0056] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0057] By building a user tag system based on user attributes, we can analyze and abstract the overall information of a user and implement user portrait analysis.

[0058] However, currently, most approaches rely on tagging dynamic data, such as user behavior, to conduct user profiling. Because infrastructure-related data is largely static, tagging systems based on this data struggle to accurately capture user attributes. Consequently, few tags exist specifically for infrastructure-related data.

[0059] To address the existing technical issues, the inventors, after considerable deliberation, cleverly designed a tag structure whose main components include infrastructure, data centers, and organizations. This tag structure, generated based on infrastructure-related data, can be used to characterize not only the data centers themselves but also the organizations themselves, enabling profiling of both data centers and organizations.

[0060] In view of this, the embodiments of the present application provide an information processing method, apparatus, device, computer storage medium, and computer program product. The information processing method provided by the embodiments of the present application is first introduced below.

[0061] Figure 1 FIG1 shows a flow chart of an information processing method provided by an embodiment of the present application. Figure 1 As shown, the information processing method may specifically include the following steps S110 to S130.

[0062] S110, obtaining first data, the first data including data metadata of a data center, the number of data centers, an energy consumption level of a data center, and a first association relationship between a data center and an organization, the first data being data obtained based on infrastructure data.

[0063] In step S110, the first data may be data obtained by calculating the infrastructure data. The infrastructure data may be data related to servers, network equipment, and security information. The data metadata of the data center may include the type and number of each data metadata tag in the data center. The energy consumption level of the data center may be obtained based on the energy consumption of the data center cabinets. As an example, the "China Green Data Center Development Report (2020)" can be referred to to classify the energy consumption levels of data centers with different numbers of cabinets according to the power usage effectiveness (PUE) of the data center. Specifically, the data center can be divided into six energy consumption levels according to the number of cabinets in the data center: <100, 100-500, 500-1000, 1000-3000, 3000-10000, and ≥10000. The first association relationship between the data center and the organization may be a corresponding relationship between the data center and the organization. Specifically, an organization may include at least one data center, and the organization has a corresponding relationship with all data centers contained in the organization.

[0064] S120: Calculate the first data to obtain the label frequency of the data center and the label frequency of the organization.

[0065] In step S120, calculating the first data may include calculating the type and quantity of each data meta tag in the data meta information of the data center based on the energy consumption level of the data center, thereby obtaining a tag frequency related to the energy consumption level of the data center. After obtaining the tag frequency of the data center, the tag frequencies of the data center may be integrated and calculated based on the tag frequency of the data center and the first association between the data center and the organization, thereby obtaining the tag frequency of the organization.

[0066] S130, based on the label frequency of the data center and the label frequency of the organization, generate a label, the label includes a first association relationship, an organization label information table, and a data center label information table, the organization label information table includes the label frequency of the organization, and the data center label information table includes the label frequency of the data center.

[0067] The organization tag information table can include tag information for multiple organizations, and the data center tag information table can include tag information for multiple data centers. It's easy to understand that, in addition to the organization's tag frequency, the organization tag information table can also include tag information such as the organization's identification information, the organization's tag identification information, and tag values. In addition to the data center's tag frequency, the data center tag information table can also include tag information such as the data center's identification information, the data center's tag identification information, and tag values.

[0068] The information processing method of the embodiments of the present application can calculate the label frequency of the data center and the label frequency of the organization based on the data metadata information of the data center obtained based on the infrastructure data, the energy consumption level of the data center, and the relationship between the data center and the organization. In this way, it can convert static infrastructure data into the label frequency of the data center and the organization. The labels generated based on the label frequency of the data center and the organization can be used for the portrait analysis of the data center and the organization, thereby depicting the infrastructure development status of the data center and the organization.

[0069] In an optional embodiment, as Figure 2 As shown, the above S120 calculates the first data to obtain the label frequency of the data center and the label frequency of the organization, which may specifically include the following steps S121 to S125.

[0070] S121, determining the original label frequency of the data center, the data meta information of the organization, and the number of organizations based on the data meta information of the data center, the number of data centers, the energy consumption level of the data center, and the first association relationship.

[0071] S122, calculating the data element information of the data center, the number of data centers, and the energy consumption level of the data center to obtain the data element weight of the energy consumption level of the data center.

[0072] S123, calculating the data element information of the organization and the number of organizations to obtain the uniform distribution weight of the data element.

[0073] S124 , performing weighted processing on the original label frequency of the data center according to the energy consumption level data element weight and the data element uniform distribution weight to obtain the label frequency of the data center.

[0074] S125 , processing the label frequency of the data center according to the first association relationship to obtain the label frequency of the organization.

[0075] In step S121, the raw tag frequency of the data center can be determined based on the total number of data meta tags in the data center. Based on the correspondence between all data centers and organizations, the number of organizations and their data meta information can be determined. Specifically, the data meta information of an organization can be the aggregate of the data meta information of all data centers corresponding to the organization.

[0076] In step S122, the data centers may be classified according to energy consumption levels. For each energy consumption level of the data center, the weight of the data element corresponding to the data center of the energy consumption level is calculated.

[0077] In step S123, the data element information of the organization and the number of organizations are calculated, and the weight of each type of data element can be determined according to the quantitative relationship between each type of data element tags in the data element information of the organization, thereby obtaining the uniform distribution weight of the data elements.

[0078] In step S124, the original label frequency of the data center is weighted according to the energy consumption level data element weight and the data element uniform distribution weight, which can include weighting the frequency of each type of data element label in the data center according to the energy consumption level of the data center to which the data element label belongs to obtain a first weighted result, and weighting the frequency of each type of data element label in the data center according to the data element uniform distribution weight corresponding to the data element label to obtain a second weighted result, and then adding the first weighted result and the second weighted result to obtain the label frequency of the data center.

[0079] Step S125 can be implemented in a variety of ways. As an example, processing the label frequency of the data center according to the first association relationship may include adding the label frequencies of multiple data centers corresponding to each organization. The label frequency of an organization may be the sum of the label frequencies of all data centers corresponding to the organization. As an example, processing the label frequency of the data center according to the first association relationship may also include integrating the original label frequencies of all data centers corresponding to the organization to obtain the original label frequency of the organization, and then weighting the original label frequency of the organization according to the energy consumption level data element weight and the data element uniform distribution weight to obtain the label frequency of the organization.

[0080] The development status of an industry can be reflected by the development of its individual institutions. Calculating the uniform distribution weights of various data elements based on their distribution within institutions can reflect the importance of each data element from the perspective of the entire industry. Calculating data element weights based on energy consumption levels can hierarchically reflect the importance of each data element to data centers at different energy consumption levels. Calculating the data center and institution label frequencies based on the uniform distribution weights and energy consumption level data element weights comprehensively considers the data center energy consumption level corresponding to the data element labels and the distribution of data element labels across the industry, thereby more accurately reflecting the infrastructure development status of data centers and institutions.

[0081] In an optional embodiment, the data center includes multiple data centers with different energy consumption levels. The data element information of the data center, the number of data centers, and the energy consumption level of the data center are calculated to obtain the energy consumption level data element weight of the data center, which may include:

[0082] Based on the data element information of the data center and the number of data centers, the average number of each type of data element at each energy consumption level is calculated.

[0083] The energy consumption level data element weight of the data center is determined according to the average number of each type of data elements at each energy consumption level.

[0084] The energy consumption level data element weight of the data center may be the weight of each type of data element label in each energy consumption level.

[0085] As an example, the average number of data elements of any type at each energy consumption level can be calculated by dividing the sum of the number of data centers at that energy consumption level and the number of all data elements in the data centers at that energy consumption level by the sum of the number of data centers at that energy consumption level and the number of data centers that possess data elements of that type. Specifically, this can be calculated using the following formula 1.

[0086]

[0087] In formula 1, n ij represents the average number of data meta-tags of type j in a data center with energy consumption level i; a i represents the number of data centers with energy consumption level i; b i represents the number of all data meta-tags in a data center with energy consumption level i; c ij It represents the number of data centers with energy consumption level i and having data meta-tag type j.

[0088] As an example, after calculating the average number of data elements of each type, determining the energy consumption level data element weight of the data center may include: for each energy consumption level of the data center, based on the average number of data elements of each type, calculating the proportion of the average number of data elements of each type in the energy consumption level, and the least common multiple of the average number of data elements of each type in the energy consumption level, and dividing the above least common multiples by the proportion of the average number of data elements of each type, to obtain the energy consumption level data element weight of each type of data element in the energy consumption level.

[0089] In this way, the weights of various data elements in each energy consumption level of the data center can be determined according to the energy consumption level of the data center, so as to more accurately reflect the impact of each type of data element in each energy consumption level on the tag body.

[0090] In an optional embodiment, calculating the data element information of the organization and the number of organizations to obtain the uniform distribution weight of the data element may include:

[0091] Based on the data element information of the organization and the number of organizations, calculate the average number of each type of data element in the organization.

[0092] Determine the uniform distribution weight of data elements based on the average number of each type of data elements in the organization.

[0093] As an example, the average number of data elements of each type in an organization can be calculated using the following formula 2.

[0094]

[0095] In formula 2, m j represents the average number of data meta-tags of type j in all institutions; A represents the number of institutions; B represents the number of all data meta-tags in the institution; C j represents the number of institutions that have the j-type data meta-label.

[0096] As an example, after calculating the average number of data elements of each category in the organization, determining the uniform distribution weight of the data elements may include: calculating the proportion of the average number of data elements of each category in the data elements of all organizations, and the least common multiple of the average number of data elements of each category based on the average number of data elements of each category, and dividing the above least common multiples by the average number of data elements of each category in the data elements of all organizations to obtain the uniform distribution weight of the data elements including each category of data elements.

[0097] In this way, the distribution weight of each type of data element in the entire industry can be obtained. The original label frequency of the data center is processed according to the uniform distribution weight of the data element. The obtained label frequency can more intuitively reflect the situation of various types of data elements in the industry.

[0098] In an optional implementation, before obtaining the first data, the method may further include:

[0099] The infrastructure data is acquired, where the infrastructure data includes data metadata of the infrastructure, a second association relationship between the infrastructure and the data center, and a first association relationship.

[0100] The data meta information of the data center, the number of data centers, and the energy consumption level of the data center are determined according to the data meta information of the infrastructure and the second association relationship.

[0101] The data meta information of the infrastructure may include the type and number of each data meta tag in the infrastructure. For example, it may include server data meta tags and their number, network device data meta tags and their number, security data meta tags and their number, etc. The second association between the infrastructure and the data center may be a correspondence between the infrastructure and the data center. Specifically, if a data center can include at least one infrastructure, then the data center has a correspondence with all infrastructure contained in the data center.

[0102] The data collection system can be used to obtain infrastructure data. The infrastructure data can be related to infrastructure reported by an organization. For example, after obtaining the infrastructure data reported by the organization, the data collection system can process the data, such as performing data analysis and data verification, and store the processed data in a database.

[0103] Determining the data center data meta information, the number of data centers, and the data center energy consumption level based on the infrastructure data meta information and the second association relationship may include: determining the data centers corresponding to all infrastructures based on the second association relationship, thereby determining the number of data centers and the data center energy consumption level; and integrating and calculating the data meta information of the infrastructure corresponding to each data center to obtain the data meta information of the data center. As an example, this step may be performed by a data lake, such as an enterprise-level data lake.

[0104] In this way, the information required to construct the label can be obtained based on the infrastructure data and the corresponding relationship between the infrastructure and the data center, so that the information contained in the constructed label is more comprehensive and specific.

[0105] In an optional embodiment, before generating a label based on the label frequency of the data center and the label frequency of the organization, the method may further include:

[0106] Obtain the infrastructure's tag information table, which includes the infrastructure's identity, tag identifier, and tag value.

[0107] Obtain the identification information of the data center, where the data center representation information includes the identity identifier of the data center and the label identifier of the data center.

[0108] Obtain the organization's identification information, which includes the organization's identity and label.

[0109] As is easy to understand, in an infrastructure tag information table, the infrastructure's identity can include identification information such as the infrastructure's name, the tag can include the infrastructure's tag name or category, and the tag value can be used to describe the tag. A data center's identity can include identification information such as the data center's name, and the data center's identification tag information can include information such as the data center's scale and energy consumption level. An organization's identity can include identification information such as the organization's name, and the organization's identification tag information can include information such as the organization's scale and region.

[0110] The infrastructure tag information table can be used to profile the infrastructure. The data center identification information can display the basic attributes of the data center itself, and the organization information can display the basic attributes of the organization itself. This allows for more comprehensive information on infrastructure, data centers, and organizations, facilitating their profile analysis.

[0111] In an optional implementation, the label may further include a label information table of the infrastructure and a second association relationship.

[0112] The institution label information table also includes the identification information of the institution.

[0113] The data center label information table also includes identification information of the data center.

[0114] The label includes the label information table and second association relationship of the above-mentioned infrastructure, the identification information of the organization, and the identification information of the data center, which can provide more comprehensive data support when performing portrait analysis on the infrastructure, data center, and organization.

[0115] In an optional embodiment, as Figure 3 As shown, the tag 300 may include a tag subject relationship information table, an infrastructure tag information table, a data center tag information table, an organization tag information table, and a data source weight information table. The first association relationship and the second association relationship may be stored in the tag subject relationship information table. The tag subject relationship information table may also include the business primary key identifier and the technical primary key identifier of each tag subject. The identification of each data element may also be stored in the organization tag information table and the data center tag information table. The tag may also include a data element weight information table to facilitate the extraction of weight information of each data element. Specifically, the data element weight information table may include a data element identifier, a weight type, and a weight value. As an example, the tag may be stored in a horizontal manner, for example, it may be stored in a key-value manner, so that high scalability of newly added tags can be achieved.

[0116] It is easy to understand that in the various embodiments described above, different data may be processed to obtain the information required for generating labels, and labels may be constructed based on the information required for generating labels. Alternatively, raw labels may be generated based on the acquired data, and the raw labels may be processed to generate labels. This application does not impose any particular limitation on this.

[0117] In an optional embodiment, after generating the label based on the label frequency of the data center and the label frequency of the organization, the method may further include:

[0118] Periodically obtain infrastructure data.

[0119] Update labels based on infrastructure data.

[0120] For example, tags can be stored in a tag library, which also includes tag metadata. This tag metadata manages basic information, lifecycle, and update frequency of the tag. As can be readily understood, tag metadata can be used to manage the periodicity of infrastructure data acquisition and tag updates.

[0121] In this way, when the infrastructure data changes, the changed data can be obtained in time and the labels can be updated. In this way, the static infrastructure data can be converted into relatively dynamic data, so that the information contained in the generated labels can be more accurate.

[0122] In an optional embodiment, after generating the label based on the label frequency of the data center and the label frequency of the organization, the method may further include:

[0123] Based on the tags, a word cloud analysis is performed on at least one of the data center and / or the organization to generate a word cloud map of the data center and / or a word cloud map of the organization.

[0124] A word cloud visually highlights frequently occurring keywords within a text, creating a "keyword cloud" or "keyword rendering." This filter eliminates significant textual information and allows viewers to intuitively grasp the main thrust of the text. Similarly, within a tag body, visually highlighting the most frequently occurring tags creates a tag word cloud for that body. This allows for visualization of data center and organization tags, allowing users to clearly and quickly identify their characteristics. Furthermore, generating word clouds for data centers and organizations allows for intuitive comparisons between organizations, as well as between multiple data centers within a single organization.

[0125] In an optional embodiment, after generating the tag based on the tag frequency of the data center and the tag frequency of the organization, the method may further include:

[0126] Based on tags, use business intelligence (BI) analysis tools to create a profile of at least one of the organization, data center, and infrastructure.

[0127] Business Intelligence (BI) analysis tools can combine tags to conduct in-depth analysis of tag entities such as organizations and data center infrastructure. By displaying the tags of each tag entity, various attributes of the tag entity can be preliminarily displayed.

[0128] In an optional embodiment, after generating the tag based on the tag frequency of the data center and the tag frequency of the organization, the method may further include:

[0129] At least one of an infrastructure, a data center, and an organization is classified according to the label.

[0130] The labels include characteristics of infrastructure, data centers, and institutions in multiple dimensions. Based on the labels, infrastructure, data centers, and institutions can be classified from multiple angles according to actual needs.

[0131] In an optional implementation, when conducting data mining on infrastructure, data centers, and institutions, it is also possible to combine Python data mining capabilities based on labels to provide features that can be quickly used by classification models, reducing the difficulty of data preprocessing in data models.

[0132] It is easy to understand that in actual applications, we can also use tags, combined with BI analysis tools, word cloud analysis, multi-angle classification of tag subjects, and Python data mining capabilities, to deeply characterize the attributes of infrastructure, data centers, and institutions, thereby comprehensively displaying the characteristics, correlations, and development status of infrastructure, data centers, and institutions.

[0133] Based on the same inventive concept, an embodiment of the present application further provides an information processing device 400 .

[0134] like Figure 4 As shown, the information processing device 400 may include an acquisition module 401 , a calculation module 402 and a generation module 403 .

[0135] The first acquisition module 401 is used to acquire first data. The first data includes data metadata of data centers, the number of data centers, the energy consumption level of data centers, and a first association relationship between data centers and organizations. The first data is data acquired based on infrastructure data.

[0136] The calculation module 402 is used to calculate the first data to obtain the label frequency of the data center and the label frequency of the organization.

[0137] The first generation module 403 is used to generate a label based on the label frequency of the data center and the label frequency of the organization. The label includes a first association relationship, an organization label information table, and a data center label information table. The organization label information table includes the label frequency of the organization, and the data center label information table includes the label frequency of the data center.

[0138] The information processing device of an embodiment of the present application can calculate the label frequency of the data center and the label frequency of the organization based on the data metadata information of the data center obtained based on the infrastructure data, the energy consumption level of the data center, and the relationship between the data center and the organization. In this way, it can convert static infrastructure data into the label frequency of the data center and the organization. The labels generated based on the label frequency of the data center and the organization can be used for data center and organization portrait analysis, thereby depicting the infrastructure development status of the data center and the organization.

[0139] In an optional implementation, the calculation module 402 is configured to calculate the first data to obtain the label frequency of the data center and the label frequency of the organization, which may include:

[0140] The determination submodule is used to determine the original label frequency of the data center, the data meta information of the organization, and the number of organizations based on the data meta information of the data center, the number of data centers, the energy consumption level of the data center, and the first association relationship.

[0141] The first calculation submodule is used to calculate the data element information of the data center, the number of data centers and the energy consumption level of the data center to obtain the energy consumption level data element weight of the data center.

[0142] The second calculation submodule is used to calculate the data element information of the organization and the number of organizations to obtain the uniform distribution weight of the data element.

[0143] The first processing submodule is configured to perform weighted processing on the original label frequency of the data center according to the energy consumption level data element weight and the data element uniform distribution weight to obtain the label frequency of the data center.

[0144] The second processing submodule is used to process the label frequency of the data center according to the first association relationship to obtain the label frequency of the organization.

[0145] In an optional embodiment, the data center may include multiple data centers with different energy consumption levels. The first calculation submodule is configured to calculate the data element information of the data center, the number of data centers, and the energy consumption level of the data center to obtain the energy consumption level data element weight of the data center, which may include:

[0146] The first calculation unit is used to calculate the average number of data elements of each type in each energy consumption level according to the data element information of the data center and the number of data centers.

[0147] The first determining unit is configured to determine the energy consumption level data element weight of the data center according to the average number of each type of data elements at each energy consumption level.

[0148] In an optional embodiment, the second calculation submodule is used to calculate the data element information of the organization and the number of organizations to obtain the uniform distribution weight of the data element, which may include:

[0149] The second calculation unit is used to calculate the average number of each type of data elements in the organization according to the data element information of the organization and the number of the organization.

[0150] The second determining unit is used to determine the uniform distribution weight of the data elements according to the average number of each type of data elements in the organization.

[0151] In an optional implementation, the information processing device 400 may further include:

[0152] The second acquisition module is used to acquire infrastructure data before acquiring the first data. The infrastructure data includes data metadata of the infrastructure, the second association relationship between the infrastructure and the data center, and the first association relationship.

[0153] The determination module is used to determine the data metadata of the data center, the number of data centers, and the energy consumption level of the data center according to the data metadata of the infrastructure and the second association relationship.

[0154] In an optional implementation, the information processing device 400 may further include:

[0155] The third acquisition module is used to obtain the infrastructure label information table before generating labels based on the label frequency of the data center and the label frequency of the organization. The infrastructure label information table includes the infrastructure identity, label identifier and label value.

[0156] The fourth acquisition module is used to obtain the identification information of the data center before generating labels based on the label frequency of the data center and the label frequency of the organization. The representation information of the data center includes the identity of the data center and the identification label information of the data center.

[0157] The fifth acquisition module is used to acquire the identification information of the organization before generating the label based on the label frequency of the data center and the label frequency of the organization. The identification information of the organization includes the identity of the organization and the identification label information of the organization.

[0158] In an optional implementation, the tag may further include a tag information table of the infrastructure and a second association relationship; the organization tag information table may further include identification information of the organization; and the data center tag information table may further include identification information of the data center.

[0159] In an optional implementation, the information processing device 400 may further include:

[0160] The sixth acquisition module is used to periodically acquire the data of the infrastructure after generating the label based on the label frequency of the data center and the label frequency of the organization.

[0161] The update module is used to update labels based on the data from the infrastructure.

[0162] In an optional implementation, the information processing device 400 may further include:

[0163] The second generation module is used to generate labels based on the label frequency of the data center and the label frequency of the organization, and then perform word cloud analysis on at least one of the data center and / or the organization according to the labels to generate a word cloud diagram of the data center and / or a word cloud diagram of the organization.

[0164] In an optional implementation, the information processing device 400 may further include:

[0165] The portrait module is used to generate labels based on the label frequency of the data center and the label frequency of the organization, and then use business intelligence BI analysis tools to profile at least one of the organization, data center, and infrastructure based on the labels.

[0166] In an optional implementation, the information processing device 400 may further include:

[0167] The classification module is used to classify at least one of the infrastructure, the data center, and the organization according to the label after generating the label based on the label frequency of the data center and the label frequency of the organization.

[0168] The information processing device provided in the embodiment of the present application can realize Figure 1 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0169] Figure 5 A schematic diagram of the hardware structure of the information processing device provided in an embodiment of the present application is shown.

[0170] The information processing device may include a processor 501 and a memory 502 storing computer program instructions.

[0171] Specifically, the processor 501 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0172] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 502 may include removable or non-removable (or fixed) media. Where appropriate, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0173] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0174] The processor 501 implements any one of the information processing methods in the above embodiments by reading and executing computer program instructions stored in the memory 502 .

[0175] As an example, the information processing device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0176] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0177] Bus 510 includes hardware, software or both, and the components of online data flow metering equipment are coupled to each other. For example, but not limitation, bus can include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 510 can include one or more buses. Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0178] The information processing device can execute the information processing method in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 4 Described information processing method and device.

[0179] In addition, in conjunction with the information processing methods in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the information processing methods in the above embodiments is implemented.

[0180] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0181] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0182] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0183] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0184] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. An information processing method, characterized in that: include: Acquire first data, where the first data includes data metadata of a data center, the number of data centers, an energy consumption level of a data center, and a first association relationship between the data center and an organization, and the first data is data acquired based on infrastructure data; Determining, based on the data metadata information of the data center, the number of the data centers, the energy consumption level of the data center, and the first association relationship, the original label frequency of the data center, the data metadata information of the organization, and the number of the organization; Calculating the data element information of the data center, the number of the data centers, and the energy consumption level of the data center to obtain the energy consumption level data element weight of the data center; Calculating the data element information of the organization and the number of the organization to obtain a uniform distribution weight of the data element; Performing weighted processing on the original label frequency of the data center according to the energy consumption level data element weight and the data element uniform distribution weight to obtain the label frequency of the data center; Processing the label frequency of the data center according to the first association relationship to obtain the label frequency of the organization; Based on the label frequency of the data center and the label frequency of the organization, a label is generated, and the label includes the first association relationship, an organization label information table, and a data center label information table. The organization label information table includes the label frequency of the organization, and the data center label information table includes the label frequency of the data center.

2. The method according to claim 1, characterized in that The data center includes a plurality of data centers with different energy consumption levels, and the calculation of the data element information of the data center, the number of the data centers, and the energy consumption level of the data center to obtain the energy consumption level data element weight of the data center includes: Calculate the average number of data elements of each type at each energy consumption level according to the data element information of the data center and the number of data centers; The energy consumption level data element weight of the data center is determined according to the average number of data elements of each type at each energy consumption level.

3. The method according to claim 1, characterized in that The calculating of the data element information of the organization and the number of the organization to obtain the uniform distribution weight of the data element includes: Calculate the average number of each type of data element in the organization based on the data element information of the organization and the number of the organization; The uniform distribution weight of the data elements is determined according to the average number of data elements of each type in the organization.

4. The method according to claim 1, wherein Before obtaining the first data, the method further includes: Acquire data of the infrastructure, where the data of the infrastructure includes data metadata of the infrastructure, a second association relationship between the infrastructure and the data center, and the first association relationship; The data meta information of the data center, the number of data centers, and the energy consumption level of the data center are determined according to the data meta information of the infrastructure and the second association relationship.

5. The method according to claim 4, characterized in that Before generating the label based on the label frequency of the data center and the label frequency of the organization, the method further includes: obtaining a label information table of the infrastructure, the label information table of the infrastructure including an identity identifier, a label identifier, and a label value of the infrastructure; Acquire identification information of the data center, where the representation information of the data center includes the identity of the data center and identification tag information of the data center; The identification information of the organization is obtained, where the identification information of the organization includes the identity identifier of the organization and the identification label information of the organization.

6. The method according to claim 5, characterized in that The label also includes a label information table of the infrastructure and the second association relationship; The institution label information table also includes identification information of the institution; The data center label information table also includes identification information of the data center.

7. The method according to any one of claims 1 to 6, characterized in that After generating the label based on the label frequency of the data center and the label frequency of the organization, the method further includes: Periodically acquiring data of the infrastructure; The tag is updated according to the data of the infrastructure.

8. The method according to any one of claims 1 to 6, characterized in that After generating the label based on the label frequency of the data center and the label frequency of the organization, the method further includes: Based on the tags, a word cloud analysis is performed on at least one of the data center and / or the organization to generate a word cloud diagram of the data center and / or a word cloud diagram of the organization.

9. The method according to claim 6, characterized in that After generating the label based on the label frequency of the data center and the label frequency of the organization, the method further includes: Based on the tags, a business intelligence (BI) analysis tool is used to create a profile of at least one of the organization, the data center, and the infrastructure.

10. The method according to claim 6, characterized in that After generating the label based on the label frequency of the data center and the label frequency of the organization, the method further includes: At least one of the infrastructure, the data center, and the organization is classified according to the label.

11. An information processing device, characterized in that: The device comprises: A first acquisition module is configured to acquire first data, wherein the first data includes data metadata of a data center, the number of data centers, an energy consumption level of a data center, and a first association relationship between the data center and an organization, and the first data is data acquired based on infrastructure data; a determination submodule, configured to determine the original label frequency of the data center, the data meta information of the organization, and the number of the organization based on the data meta information of the data center, the number of the data centers, the energy consumption level of the data center, and the first association relationship; A first calculation submodule is configured to calculate the data element information of the data center, the number of the data centers, and the energy consumption level of the data center to obtain the energy consumption level data element weight of the data center; A second calculation submodule is used to calculate the data element information of the organization and the number of the organization to obtain a uniform distribution weight of the data element; a first processing submodule, configured to perform weighted processing on the original label frequency of the data center according to the energy consumption level data element weight and the data element uniform distribution weight, so as to obtain the label frequency of the data center; A second processing submodule is configured to process the label frequency of the data center according to the first association relationship to obtain the label frequency of the organization; The first generation module is used to generate a label based on the label frequency of the data center and the label frequency of the organization. The label includes the first association relationship, an organization label information table, and a data center label information table. The organization label information table includes the label frequency of the organization, and the data center label information table includes the label frequency of the data center.

12. An information processing device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the information processing method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the information processing method according to any one of claims 1 to 10 is implemented.

14. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the information processing method according to any one of claims 1 to 10.

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