CN114167965B - High-heat-density intelligent refrigerating method and system based on data center - Google Patents
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Abstract
According to the high heat density intelligent refrigeration method and system based on the data center, the description temperature characteristic vector globally corresponding to the description strategy of the heat data to be processed is generated through the important temperature characteristic vector corresponding to the heat data types of the plurality of objects and the projection distribution refrigeration matrix for representing the projection relation among the heat data types of the objects, and the heat data temperature characteristic vector is generated through the heat data contained in the heat data to be described.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a high-heat-density intelligent refrigeration method and system based on a data center.
Background
With the continuous increase of functions of the data center, the functional equipment in the data center is continuously increased, so that the heat productivity of the data center is easily caused to be too large, and the functional equipment is in fault. Therefore, a refrigerating method is urgently needed to solve the problem of overheat of the data center, so that functional equipment can be effectively protected to work safely, and the working efficiency is improved.
Disclosure of Invention
In view of this, the application provides a data center-based intelligent high heat density refrigeration method and system.
In a first aspect, a data center-based intelligent high heat density refrigeration method is provided, including:
Acquiring a description strategy of heat data to be processed and a description temperature characteristic vector globally corresponding to the description strategy of heat data to be processed, wherein the description strategy of heat data to be processed is divided into a plurality of categories according to the categories of heat data of objects, and the description temperature characteristic vector is generated based on important temperature characteristic vectors corresponding to the categories of heat data of the objects and a projection distribution refrigeration matrix for representing projection relations among the categories of heat data of the objects;
generating a heat data temperature characteristic vector based on heat data contained in heat data to be described;
And judging a heat data description strategy to which the heat data to be described belongs from the heat data description strategy to be processed based on the heat data temperature characteristic vector and the description temperature characteristic vector.
Further, the obtaining the description temperature feature vector globally corresponding to the description policy of the heat data to be processed includes:
acquiring important temperature characteristic vectors corresponding to a plurality of object heat data categories, and acquiring a projection distribution refrigeration matrix containing projection relations among the plurality of object heat data categories;
splicing the important temperature characteristic vectors corresponding to the heat data types of the objects to generate an object characteristic distribution refrigeration matrix;
And generating a description temperature characteristic vector globally corresponding to the to-be-processed heat data description strategy based on the object characteristic distribution refrigeration matrix and the projection distribution refrigeration matrix.
Further, the obtaining important temperature feature vectors corresponding to the heat data categories of the plurality of objects includes:
respectively acquiring a target historical object containing each object heat data category aiming at each object heat data category;
for each object heat data category, respectively carrying out important content identification on the target historical object to obtain a sample important temperature characteristic vector corresponding to the target historical object;
and generating an important temperature characteristic vector corresponding to each object heat data category based on the standard identification index of the sample important temperature characteristic vector corresponding to the target historical object.
Further, the obtaining a projection distribution refrigeration matrix containing projection relations among the object heat data categories comprises:
Determining the relevance between every two object heat data categories in the plurality of object heat data categories;
based on the correlation, a projection distribution refrigeration matrix for representing projection relationships between object heat data categories is generated.
Further, the generating a heat data temperature feature vector based on the heat data included in the heat data to be described includes:
Generating an important temperature characteristic vector corresponding to the object heat data based on the object heat data contained in the heat data to be described, and generating an important temperature characteristic vector corresponding to the important heat data based on the important heat data contained in the heat data to be described;
and performing feature stitching on the important temperature feature vector and the important temperature feature vector to generate a heat data temperature feature vector.
Further, the important temperature characteristic vector corresponding to the object heat data is generated through a preconfigured training thread, and the high heat density intelligent refrigeration method based on the data center further comprises the following steps:
Acquiring an object to be processed, which comprises an object heat data category corresponding to the heat data description strategy to be processed;
Selecting the object to be processed to generate a selected object; generating a configuration sample description mode set based on the selected object and the object to be processed, wherein each sample description mode in the configuration sample description mode set comprises a history object and a heat data description strategy to be processed to which the history object belongs;
And configuring the training thread to be configured based on the configuration sample description mode set to obtain the pre-configured training thread.
Further, the generating, based on the important heat data included in the heat data to be described, an important temperature feature vector corresponding to the important heat data includes:
Carrying out identification processing on important heat data contained in the heat data to be described to obtain an identification result corresponding to the important heat data and a simulation mode of each description mode contained in the identification result;
generating a mode temperature characteristic vector corresponding to each description mode based on the identification result, and generating a mode temperature characteristic vector based on a simulation mode of each description mode contained in the identification result where the identification result is;
and performing splicing processing on the mode temperature characteristic vector corresponding to each description mode based on the mode temperature characteristic vector to generate an important temperature characteristic vector containing a global simulation state.
Further, the determining, in the to-be-processed heat data description policy, a heat data description policy to which the to-be-described heat data belongs based on the heat data temperature feature vector and the description temperature feature vector includes:
Judging the description judgment importance degree when the heat data to be described belongs to the heat data description strategy to be processed based on the heat data temperature characteristic vector and the description temperature characteristic vector;
Selecting a heat data description strategy to be processed with the largest description judgment importance degree from the heat data description strategies to be processed based on the description judgment importance degree;
And if the description judgment importance degree corresponding to the selected heat data description strategy to be processed meets a preset description judgment importance degree threshold, taking the selected heat data description strategy to be processed as the heat data description strategy to which the heat data to be described belongs.
Further, after the heat data description policy to which the heat data to be described belongs is determined in the heat data description policy to be processed based on the heat data temperature feature vector and the description temperature feature vector, the data center-based high heat density intelligent refrigeration method further includes:
if the description heat characteristics of the object do not have the characteristics matched with the heat data description strategy to which the heat data to be described belongs, loading the heat data to be described into a main mode of refusing to implement the heat data to be described;
And if the feature matched with the hot data description strategy to which the hot data to be described belongs exists in the hot description features of the object, loading the hot data to be described into a hot data secondary mode for implementing the hot data to be described to the object.
In a second aspect, a data center based high heat density intelligent refrigeration system is provided comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method described above.
According to the high heat density intelligent refrigeration method and system based on the data center, the description temperature characteristic vector globally corresponding to the to-be-processed heat data description strategy is generated through the projection distribution refrigeration matrix based on the important temperature characteristic vectors corresponding to the heat data types of the plurality of objects and the projection relation between the heat data types of the objects, and the heat data temperature characteristic vector is generated through the heat data contained in the to-be-described heat data.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a high heat density intelligent refrigeration method based on a data center according to an embodiment of the present application.
Fig. 2 is a block diagram of a high heat density intelligent refrigeration device based on a data center according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a high heat density intelligent refrigeration system based on a data center according to an embodiment of the present application.
Detailed Description
In order to better understand the above technical solutions, the following detailed description of the technical solutions of the present application is made by using the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and the embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and the technical features of the embodiments and the embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a data center-based intelligent high heat density refrigeration method is shown, which may include the following steps 100-300.
Step 100, acquiring a description strategy of heat data to be processed and a description temperature feature vector globally corresponding to the description strategy of heat data to be processed, wherein the description strategy of heat data to be processed is divided into a plurality of categories according to the categories of heat data of the object, and the description temperature feature vector is generated based on important temperature feature vectors corresponding to the categories of heat data of the object and a projection distribution refrigeration matrix for representing projection relations among the categories of heat data of the object.
For example, the pending hot data description policy indicates the relevant data that needs to be described.
And 200, generating a heat data temperature characteristic vector based on heat data contained in the heat data to be described.
For example, the heat data temperature feature vector represents how the related data corresponds to a single description.
And 300, judging a heat data description strategy to which the heat data to be described belongs from the heat data description strategy to be processed based on the heat data temperature characteristic vector and the description temperature characteristic vector.
For example, the hot data description policy indicates the corresponding description manner.
It can be understood that when the technical solutions described in the above steps 100 to 300 are executed, the description temperature feature vector globally corresponding to the description policy of the heat data to be processed is generated based on the important temperature feature vectors corresponding to the heat data types of the objects and the projection distribution refrigeration matrix for representing the projection relationship between the heat data types of the objects, and the heat data temperature feature vector is generated through the heat data included in the heat data to be described.
In an alternative embodiment, the inventor finds that when the description temperature feature vector globally corresponding to the to-be-processed heat data description policy is obtained, there is a problem that the important temperature feature vector is inaccurate, so that it is difficult to accurately obtain the description temperature feature vector globally corresponding to the to-be-processed heat data description policy, and in order to improve the technical problem, the step of obtaining the description temperature feature vector globally corresponding to the to-be-processed heat data description policy described in step 100 may specifically include the following technical scheme described in step q 1-step q 3.
And q1, acquiring important temperature characteristic vectors corresponding to a plurality of object heat data categories, and acquiring a projection distribution refrigeration matrix containing projection relations among the plurality of object heat data categories.
And q2, performing splicing processing on the important temperature characteristic vectors corresponding to the heat data types of the objects to generate an object characteristic distribution refrigeration matrix.
And q3, generating a description temperature characteristic vector globally corresponding to the description strategy of the heat data to be processed based on the object characteristic distribution refrigeration matrix and the projection distribution refrigeration matrix.
It can be understood that when the technical scheme described in the step q 1-step q3 is executed, the problem of inaccuracy of the important temperature feature vector is avoided when the description temperature feature vector corresponding to the overall state of the heat data description strategy to be processed is obtained, so that the description temperature feature vector corresponding to the overall state of the heat data description strategy to be processed can be accurately obtained.
In an alternative embodiment, the inventor finds that obtaining important temperature feature vectors corresponding to a plurality of object heat data types has a problem that each object heat data type is inaccurate, so that it is difficult to accurately obtain important temperature feature vectors corresponding to a plurality of object heat data types, and in order to improve the technical problem, the step of obtaining important temperature feature vectors corresponding to a plurality of object heat data types described in step q1 may specifically include the following technical scheme described in steps q1a 1-q 1a 3.
Step q1a1, for each object heat data category, obtaining a target history object containing the object heat data category.
And q1a2, respectively carrying out important content identification on the target historical object according to each object heat data category to obtain a sample important temperature characteristic vector corresponding to the target historical object.
And q1a3, generating an important temperature characteristic vector corresponding to each object heat data category based on the standard identification index of the sample important temperature characteristic vector corresponding to the target historical object.
It can be understood that when the technical scheme described in the steps q1a1 to q1a3 is executed, important temperature feature vectors corresponding to a plurality of object heat data types are obtained, so that the problem of inaccuracy of each object heat data type is avoided, and therefore, the important temperature feature vectors corresponding to a plurality of object heat data types can be accurately obtained.
In an alternative embodiment, the inventor finds that when obtaining the projection distribution refrigeration matrix including the projection relationship between the plurality of object heat data categories, there is a problem that the matching between every two object heat data categories is inaccurate, so that it is difficult to accurately obtain the projection distribution refrigeration matrix including the projection relationship between the plurality of object heat data categories, and in order to improve the technical problem, the step of obtaining the projection distribution refrigeration matrix including the projection relationship between the plurality of object heat data categories described in the step q1 may specifically include the following technical solutions described in the step q1b1 and the step q1b 2.
And step q1b1, determining the relevance between every two object heat data categories in the plurality of object heat data categories.
And step q1b2, based on the relevance, generating a projection distribution refrigeration matrix for representing the projection relation between the object heat data categories.
It can be understood that when the technical solutions described in the above steps q1b1 and q1b2 are executed, the problem of inaccurate matching between every two object heat data categories is avoided when the projection distribution refrigeration matrix including the projection relationship between the plurality of object heat data categories is obtained, so that the projection distribution refrigeration matrix including the projection relationship between the plurality of object heat data categories can be accurately obtained.
In an alternative embodiment, the inventor finds that when the heat data is included in the heat data to be described, there is a problem that the important temperature feature vector is not accurate, so that it is difficult to accurately generate the heat data temperature feature vector, and in order to improve the technical problem, the step of generating the heat data temperature feature vector based on the heat data included in the heat data to be described in step 200 may specifically include the following technical solutions described in step w1 and step w 2.
Step w1, generating an important temperature characteristic vector corresponding to the object heat data based on the object heat data contained in the heat data to be described, and generating an important temperature characteristic vector corresponding to the important heat data based on the important heat data contained in the heat data to be described.
And step w2, performing feature stitching on the important temperature feature vector and the important temperature feature vector to generate a heat data temperature feature vector.
It can be appreciated that when the technical solutions described in the above steps w1 and w2 are executed, the problem of inaccuracy of the important temperature feature vector is avoided based on the heat data included in the heat data to be described, so that the heat data temperature feature vector can be accurately generated.
Based on the above basis, the important temperature feature vector corresponding to the object heat data is generated through a pre-configured training thread, and the technical scheme described in the following steps e 1-e 3 can be further included.
And e1, acquiring the to-be-processed object containing the object heat data category corresponding to the to-be-processed heat data description strategy.
Step e2, selecting the object to be processed to generate a selected object; and generating a configuration sample description mode set based on the selected object and the object to be processed, wherein each sample description mode in the configuration sample description mode set comprises a history object and a heat data description strategy to be processed to which the history object belongs.
And e3, configuring the training thread to be configured based on the configuration sample description mode set to obtain the pre-configured training thread.
It can be appreciated that when the technical solutions described in the above steps e1 to e3 are executed, the pre-configured training thread can be accurately obtained by accurately selecting the object to be processed.
In an alternative embodiment, the inventor finds that, based on the important heat data included in the heat data to be described, there is a problem that the simulation mode of each description mode where the identification result is located is inaccurate, so that it is difficult to accurately generate the important temperature feature vector corresponding to the important heat data, and in order to improve the technical problem, the step of generating the important temperature feature vector corresponding to the important heat data based on the important heat data included in the heat data to be described in the step w1 may specifically include the following technical scheme described in the step w1a1 to the step w1a 3.
And step w1a1, carrying out identification processing on the important heat data contained in the heat data to be described to obtain an identification result corresponding to the important heat data and a simulation mode of each description mode contained in the identification result in which the identification result is located.
And step w1a2, generating a mode temperature characteristic vector corresponding to each description mode based on the identification result, and generating a mode temperature characteristic vector based on a simulation mode of each description mode contained in the identification result where the identification result is.
And step w1a3, performing splicing processing on the mode temperature characteristic vector corresponding to each description mode based on the mode temperature characteristic vector, and generating an important temperature characteristic vector containing a global simulation state.
It can be understood that when the technical schemes described in the steps w1a1 to w1a3 are executed, based on the important heat data included in the heat data to be described, the problem that the simulation mode of each description mode in the identification result is inaccurate is avoided, so that the important temperature feature vector corresponding to the important heat data can be accurately generated.
In an alternative embodiment, the inventor finds that, based on the heat data temperature feature vector and the description temperature feature vector, when judging a heat data description policy to which the heat data to be described belongs in the heat data description policy to be processed, there is a problem that the description importance degree is inaccurate, so that it is difficult to accurately judge the heat data description policy to which the heat data to be described belongs, and in order to improve the technical problem, the step of judging, in the heat data description policy to be processed, the heat data description policy to which the heat data to be described belongs based on the heat data temperature feature vector and the description temperature feature vector described in step 300 may specifically include the technical scheme described in the following steps t1 to t 3.
And step t1, judging the description judgment importance degree when the heat data to be described belongs to the heat data description strategy to be processed based on the heat data temperature characteristic vector and the description temperature characteristic vector.
And step t2, selecting the heat data description strategy to be processed with the maximum description and judgment importance degree from the heat data description strategies to be processed based on the description and judgment importance degree.
And step t3, if the description judgment importance degree corresponding to the selected heat data description strategy to be processed meets the preset description judgment importance degree threshold, taking the selected heat data description strategy to be processed as the heat data description strategy to which the heat data to be described belongs.
It can be understood that when the technical scheme described in the steps t1 to t3 is executed, based on the heat data temperature feature vector and the description temperature feature vector, when judging the heat data description policy to which the heat data to be described belongs in the heat data description policy to be processed, the problem of inaccurate description importance degree is avoided, so that the heat data description policy to which the heat data to be described belongs can be accurately judged.
Based on the above-mentioned basis, after the heat data description policy to which the heat data to be described belongs is determined in the heat data description policy to be processed based on the heat data temperature feature vector and the description temperature feature vector, the following technical solutions described in step y1 and step y2 may be further included.
And step y1, if the characteristic matched with the hot data description strategy to which the hot data to be described belongs does not exist in the hot description characteristics of the object, loading the hot data to be described into a main mode of rejecting the hot data to be implemented to the object.
And step y2, if the feature matched with the hot data description strategy to which the hot data to be described belongs exists in the hot description features of the object, loading the hot data to be described into a hot data secondary mode for implementing the hot data to the object.
It will be appreciated that in executing the technical solutions described in the above steps y1 and y2, by accurately analyzing the characteristic of the heat, the heat data to be described can be accurately loaded as a secondary way of dividing the heat data into the objects.
On the basis of the above, please refer to fig. 2 in combination, there is provided a high heat density intelligent refrigeration apparatus 200 based on a data center, applied to a data terminal, the apparatus comprising:
The policy obtaining module 210 is configured to obtain a description policy of heat data to be processed, and obtain a description temperature feature vector globally corresponding to the description policy of heat data to be processed, where the description policy of heat data to be processed is a plurality of classes obtained by splitting according to the classes of heat data of objects, and the description temperature feature vector is generated based on important temperature feature vectors corresponding to the classes of heat data of the objects and a projection distribution refrigeration matrix for representing projection relationships between the classes of heat data of the objects;
the feature generation module 220 is configured to generate a heat data temperature feature vector based on heat data included in the heat data to be described;
The policy determining module 230 is configured to determine, in the to-be-processed heat data description policy, a heat data description policy to which the to-be-described heat data belongs, based on the heat data temperature feature vector and the description temperature feature vector.
On the basis of the above, referring to fig. 3 in combination, there is shown a data center-based high heat density intelligent refrigeration system 300 comprising a processor 310 and a memory 320 in communication with each other, the processor 310 being configured to read and execute a computer program from the memory 320 to implement the method described above.
On the basis of the above, there is also provided a computer readable storage medium on which a computer program stored which, when run, implements the above method.
In summary, based on the above scheme, through generating the description temperature feature vector corresponding to the overall state of the to-be-processed heat data description strategy based on the important temperature feature vector corresponding to the heat data types of the plurality of objects and the projection distribution refrigeration matrix for representing the projection relation between the heat data types of the objects, and generating the heat data temperature feature vector through the heat data contained in the heat data to be described, because the heat data temperature feature vector reflects the feature information description of the heat data to be described, the temperature feature vector is the feature information for characterizing different description modes, more accurate description and identification of the heat data to be described can be realized through describing the temperature feature vector and the heat data temperature feature vector, and the accuracy of heat data description of the heat data to be described is effectively improved, thereby improving the refrigeration effect.
It should be appreciated that the systems and modules thereof shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system of the present application and its modules may be implemented not only with hardware circuitry such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software executed by various types of processors, for example, and with a combination of the above hardware circuitry and software (e.g., firmware).
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, C#, VB NET, python, and the like, a conventional programming language such as the C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject application requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the numbers allow for adaptive variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations in some embodiments for use in determining the breadth of the range, in particular embodiments, the numerical values set forth herein are as precisely as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this application if there is a discrepancy or conflict between the description, definition, and/or use of the term in the appended claims.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the application may be considered in keeping with the teachings of the application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly described and depicted herein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. The intelligent high-heat-density refrigerating method based on the data center is characterized by comprising the following steps of:
Acquiring a description strategy of heat data to be processed and a description temperature characteristic vector globally corresponding to the description strategy of heat data to be processed, wherein the description strategy of heat data to be processed is divided into a plurality of categories according to the categories of heat data of objects, and the description temperature characteristic vector is generated based on important temperature characteristic vectors corresponding to the categories of heat data of the objects and a projection distribution refrigeration matrix for representing projection relations among the categories of heat data of the objects;
generating a heat data temperature characteristic vector based on heat data contained in heat data to be described;
judging a heat data description strategy to which the heat data to be described belongs from the heat data description strategy to be processed based on the heat data temperature characteristic vector and the description temperature characteristic vector;
the obtaining the description temperature feature vector globally corresponding to the description strategy of the heat data to be processed includes:
acquiring important temperature characteristic vectors corresponding to a plurality of object heat data categories, and acquiring a projection distribution refrigeration matrix containing projection relations among the plurality of object heat data categories;
splicing the important temperature characteristic vectors corresponding to the heat data types of the objects to generate an object characteristic distribution refrigeration matrix;
And generating a description temperature characteristic vector globally corresponding to the to-be-processed heat data description strategy based on the object characteristic distribution refrigeration matrix and the projection distribution refrigeration matrix.
2. The data center-based high heat density intelligent refrigeration method according to claim 1, wherein the obtaining important temperature feature vectors corresponding to a plurality of object heat data categories comprises:
respectively acquiring a target historical object containing each object heat data category aiming at each object heat data category;
for each object heat data category, respectively carrying out important content identification on the target historical object to obtain a sample important temperature characteristic vector corresponding to the target historical object;
and generating an important temperature characteristic vector corresponding to each object heat data category based on the standard identification index of the sample important temperature characteristic vector corresponding to the target historical object.
3. The data center-based high heat density intelligent cooling method of claim 1, wherein the obtaining a projection distribution cooling matrix containing projection relationships between the plurality of object heat data categories comprises:
Determining the relevance between every two object heat data categories in the plurality of object heat data categories;
based on the correlation, a projection distribution refrigeration matrix for representing projection relationships between object heat data categories is generated.
4. The data center-based high heat density intelligent refrigeration method of claim 1, wherein generating a heat data temperature feature vector based on heat data contained in heat data to be described comprises:
Generating an important temperature characteristic vector corresponding to the object heat data based on the object heat data contained in the heat data to be described, and generating an important temperature characteristic vector corresponding to the important heat data based on the important heat data contained in the heat data to be described;
and performing feature stitching on the important temperature feature vector and the important temperature feature vector to generate a heat data temperature feature vector.
5. The data center-based high heat density intelligent cooling method according to claim 4, wherein the important temperature feature vector corresponding to the object heat data is generated through a pre-configured training thread, and further comprising:
Acquiring an object to be processed, which comprises an object heat data category corresponding to the heat data description strategy to be processed;
Selecting the object to be processed to generate a selected object; generating a configuration sample description mode set based on the selected object and the object to be processed, wherein each sample description mode in the configuration sample description mode set comprises a history object and a heat data description strategy to be processed to which the history object belongs;
And configuring the training thread to be configured based on the configuration sample description mode set to obtain the pre-configured training thread.
6. The data center-based high heat density intelligent refrigeration method according to claim 4, wherein the generating the important temperature feature vector corresponding to the important heat data based on the important heat data included in the heat data to be described comprises:
Carrying out identification processing on important heat data contained in the heat data to be described to obtain an identification result corresponding to the important heat data and a simulation mode of each description mode contained in the identification result;
generating a mode temperature characteristic vector corresponding to each description mode based on the identification result, and generating a mode temperature characteristic vector based on a simulation mode of each description mode contained in the identification result where the identification result is;
and performing splicing processing on the mode temperature characteristic vector corresponding to each description mode based on the mode temperature characteristic vector to generate an important temperature characteristic vector containing a global simulation state.
7. The data center-based high heat density intelligent refrigeration method according to claim 1, wherein the judging a heat data description policy to which the heat data to be described belongs in the heat data description policy to be processed based on the heat data temperature feature vector and the description temperature feature vector comprises:
Judging the description judgment importance degree when the heat data to be described belongs to the heat data description strategy to be processed based on the heat data temperature characteristic vector and the description temperature characteristic vector;
Selecting a heat data description strategy to be processed with the largest description judgment importance degree from the heat data description strategies to be processed based on the description judgment importance degree;
And if the description judgment importance degree corresponding to the selected heat data description strategy to be processed meets a preset description judgment importance degree threshold, taking the selected heat data description strategy to be processed as the heat data description strategy to which the heat data to be described belongs.
8. The data center-based high heat density intelligent cooling method according to claim 1, wherein after the heat data description policy to which the heat data to be described belongs is judged in the heat data description policy to be processed based on the heat data temperature feature vector and the description temperature feature vector, the data center-based high heat density intelligent cooling method further comprises:
if the description heat characteristics of the object do not have the characteristics matched with the heat data description strategy to which the heat data to be described belongs, loading the heat data to be described into a main mode of refusing to implement the heat data to be described;
And if the feature matched with the hot data description strategy to which the hot data to be described belongs exists in the hot description features of the object, loading the hot data to be described into a hot data secondary mode for implementing the hot data to be described to the object.
9. A data center based high heat density intelligent refrigeration system comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1-8.
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| CN107831866A (en) * | 2017-11-27 | 2018-03-23 | 国家计算机网络与信息安全管理中心 | A kind of water-cooling system state evaluating method, equipment and computer-readable recording medium |
| CN113608596A (en) * | 2021-07-29 | 2021-11-05 | 上海德衡数据科技有限公司 | Intelligent cooling method and system for server |
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| WO2013176681A1 (en) * | 2012-05-25 | 2013-11-28 | Hewlett-Packard Development Company, L.P. | Providing a model of impact of a cooling infrastructure |
| JP2018018226A (en) * | 2016-07-26 | 2018-02-01 | 富士通株式会社 | Control method, control program, and information processing apparatus |
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| CN107831866A (en) * | 2017-11-27 | 2018-03-23 | 国家计算机网络与信息安全管理中心 | A kind of water-cooling system state evaluating method, equipment and computer-readable recording medium |
| CN113608596A (en) * | 2021-07-29 | 2021-11-05 | 上海德衡数据科技有限公司 | Intelligent cooling method and system for server |
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